CHANGELOG.md

Changelog

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog and this project adheres to Semantic Versioning.

[0.27.2] - 2026-06-04

Added

• Cooperative abort flag to cancel a running strategy early. with_abort_flag(Arc<AtomicBool>) on Evolve, HillClimb, Permutate and the superset StrategyBuilder; the run checks it cooperatively and returns the best chromosome so far, also short-circuiting the call_repeatedly/call_speciated multi-run calls
• Add with_target_fitness_score ending condition to Permutate, stopping before the full permutation space is exhausted. Added mainly for internal "finished = conclusive or exhausted" structure

[0.27.1] - 2026-02-26

Changed

• Update lru dependency from v0.12.4 to v0.16.3
• Fix bug in UniqueGenotype: neighbouring_population_size panics when genes_size < 2
• Manually review AGENTS.md as AI generation of the document made too many assumptions

[0.27.0] - 2026-02-23

Changed

• Compile-time crossover safety. Split EvolveGenotype into an empty marker trait + separate SupportsGeneCrossover and SupportsPointCrossover traits. Removed runtime has_crossover_indexes/has_crossover_points methods
• Framework-owned age lifecycle. Moved state.population.increment_age() from crossover into the evolve loop (just before crossover call).
• Sensible defaults: target_population_size from 0 to 100 for Evolve, replace_on_equal_fitness to true for all
• Auto-disable genes_hashing in HillClimb as it is never sensible

Added

• Added fitness_value(float, precision) helper method to convert float to isize (FitnessValue)
• Better validations and error messages

[0.26.1] - 2026-02-16

Added

• Documentation additions for AI agent (and human) consumption:
  • Added AGENTS.md as a comprehensive agent guide (decision matrices, API reference, copy-paste templates, gotchas)
  • Added docstrings to all previously undocumented public constructors and key methods
  • Fixed several inaccurate docstrings (extensions, crossover, mutate, select)
  • Improved error messages with actionable fix suggestions
  • Add example headers
  • Add a new Heterogeneous Genotype example (examples/evolve_heterogeneous)

[0.26.0] - 2025-11-24

Caution

Custom Crossover implementations need to be updated to increment the age of chromosomes in their implementations of call() (e.g. state.population.increment_age())

Added

• Add multiple extension points to the Evolve flow in the Extension type
  • after_selection_complete()
  • after_crossover_complete()
  • after_mutation_complete()
  • after_generation_complete()
• Add symmetrical reporter hooks, called before the extension hooks
  • on_selection_complete() (existing)
  • on_crossover_complete()
  • on_mutation_complete()
  • on_generation_complete() (existing)
• Add before() and after() hooks to Select, Crossover, and Mutate traits
  • Allows full customization of population lifecycle operations in Evolve
  • Default implementations handle age filtering and cardinality updates

Changed

• Extension trait methods now receive &mut Self::Genotype instead of &Self::Genotype
  • Allows extensions to modify genotype parameters during evolution
• Extension trait call() method is now after_selection_complete()
  • call() still exists and delegates to after_selection_complete() for backward compatibility
• Move population lifecycle operations from inline EvolveState methods to trait hooks
  • Age filtering moved to Select::before()
  • Population cardinality update moved to Select::after()
  • Age increment handled internally by crossover implementations

[0.25.1] - 2025-11-12

Changed

• Fix github build dependencies, as v0.25.0 was marked as failed to build due to missing libfontconfig1 dependency for plotter. No changes to code.

[0.25.0] - 2025-11-12

Changed

• (naming only) Refactor MutationType::ScaledSteps(Vec<T>) to MutationType::StepScaled(Vec<T>) for naming consistency
• (naming only) Refactor MutationType::RelativeRange(T) to MutationType::Range(T), as MutationType::Step(T) is also relative.
• Refactor MutationType::Transition(usize, usize, T) to MutationType::RangeScaled(Vec<T>):
  • Drop the generation based transition configuration params (as it couldn't handle staleness)
  • Replace params with bandwidth per scale (just like step-size per scale in MutationType::StepScaled)
  • Use bandwidths equal to the full allele_range to achieve effective MutationType::Random mutation
  • Now max_generations and max_stale_generations handle scale transitions in the same manner everywhere
  • It is a good idea to use a prolonged period of full Random sampling (exploration phase, so several 100% bandwidth scales), which then transitions quite fast to the smaller bandwidths (exploitation phase, a few smaller bandwidth scales).

Added

• Add MutationType::Step(T) for completeness
• Add max_generations as scale trigger as well in Evolve and HillClimb. Now max_generations and max_stale_generations behave the same:
  • Ending condition for current scale, go to next scale and restart counting generations
  • Final ending condition if no scales left (or no scaling at all)
  • Both max_generations and max_stale_generations can be set at the same time, the first to reach the condition triggers and reset both
• Support unsigned integers for MutationType::Range and MutationType::Step as well (and scaled versions)
• Allow for zero bandwidth/step in MutationType::Range and MutationType::Step (and scaled versions), which lets the mutation die out (more for robustness than usefulness)
• Add examples/visualize_evolve_mutation_types and examples/visualize_permutate_mutation_types, which generate visualizations showing exploration patterns of different mutation strategies

Removed

• Remove increment_generation() and reset_generation() from Genotype and remove hook in Strategies, as all scaled MutationTypes now work in the same manner
• Remove unused Vec<Chromosome<T>> into Population<T> implementation

[0.24.0] - 2025-11-05

Changed

• Change MutationType::Scaled(Vec<RangeInclusive<T>) to MutationType::ScaledSteps(Vec<T>)
  • removes the range as the steps are symmetrical by definition: -T, +T (also less error prone)
  • removes potential confusion about inner range samping v. step up/down behaviour by explicit naming
• Change MutationType::Relative(RangeInclusive<T>) to MutationType::RelativeRange(T)
  • removes the range as it is symmetrical by definition, only the bandwidth is needed to make a [-T,T] range (also less error prone)
  • removes potential confusion about inner range samping v. step up/down behaviour by explicit naming
• Change MutationType::Transition(usize, usize, RangeInclusive<T>) to MutationType::Transition(usize, usize, T)
  • follow the MutationType::RelativeRange parameters

[0.23.0] - 2025-11-04

New MutationType::Transition

Transition(random_until_generation, relative_from_generation, relative_mutation_range) Smoothly transitions from Random to Relative mutation over a specified generation range. Provides a natural exploration-to-exploitation schedule without abrupt behavioral changes.

Parameters:

• random_until_generation: Use pure Random mutation until this generation
• relative_from_generation: Use pure Relative mutation from this generation on
• relative_mutation_range: The final relative range to use after transition

Example: Transition(100, 500, -5.0..=5.0) means:

• Generations 0-99: Pure Random mutation (full exploration)
• Generations 100-499: Gradual transition with decreasing mutation range
• Generations 500+: Pure Relative mutation with ±5.0 range (exploitation)

Use case: Problems requiring initial exploration followed by convergence, evolutionary algorithms with scheduled exploration decay, parameter optimization where good regions are unknown initially.

Added

• Implement MutationType::Transition for RangeGenotype and MultiRangeGenotype
• Add associated type Genotype to Extension as well for better custom implementations (just like Mutate & Crossover)
• Add associated type Genotype to Select as well for symmetry reasons (it was the only one without associated type left)
• Add increment_generation() and reset_generation() to Genotype and hook up in Strategies (for keeping track of MutationType::Transition progess inside the genotype)
• Add SelectEvent(String) and CrossoverEvent(String) tuple struct and handlers to reporting (on_select_event & on_crossover_event)

Changed

• Add mutation details to MutationType enum as tuple structs:
  • Relative now holds the mutation range for a gene: MutationType::Relative(RangeInclusive<T>
  • Scaled now holds the mutation scales for a gene: MutationType::Scaled(Vec<RangeInclusive<T>>)
  • Random and Discrete don't have details, so they stay the same
  • .with_mutation_type(s) replaces the deprecated with_allele_mutation_range(s) and with_allele_mutation_scaled_range(s) builder steps, now able to hold all the mutation details by itself
• Remove MutateEvent and ExtensionEvent enums, all events are just plain texts wrapped in a single MutateEvent(String) or ExtensionEvent(String) tuple struct
• Rename Genotype custom method sample_allele to sample_gene_random for symmetry reasons with sample_gene_delta, even though technically it is not always a gene (no index)
• Drop u64, u128, usize, i64, i128 & isize support for RangeAllele and add more numeric Traits (Add,Sub,AddAssign,Into<f64>,SampleUniform) to allow for more arithmetic in the genotype
• Rename on_new_generation reporting event in Evolve to on_selection_complete to clearly state intent. Rename other on_new_generation reporting events to on_generation_complete (HillClimb, Permutate and new for Evolve). For Evolve the on_selection_complete hook, is a more interesting point in the loop, as the population and cardinality are refreshed after selection. The on_generation_complete hook contains all the new mutations, most of which will be immediately selected out. This gives the false impression of good cardinality while there is actually little.

Removed

• Drop Genotype's gene_slice method as all genes are owned anyway

Deprecated

• with_allele_mutation_range & with_allele_mutation_scaled_range now have deprecation warnings, as they are replaced by with_mutation_type
• with_allele_mutation_ranges & with_allele_mutation_scaled_ranges now have deprecation warnings, as they are replaced by with_mutation_types

[0.22.0] - 2025-10-30

Heterogeneous Genotype Support

MultiRangeGenotype now supports heterogeneous chromosomes that mix different gene semantics (continuous values, discrete choices, booleans) within a single numeric type T. See MultiRangeGenotype documentation for example.

Use .with_mutation_types(vec![...]) to specify behavior for each gene individually:

• MutationType::Random - Samples uniformly from the full allele range
• MutationType::Relative - Mutates within a relative range around the current value (set for gene through allele_mutation_ranges)
• MutationType::Scaled - Progressive refinement through multiple scale levels around the current value (set for gene through allele_mutation_scaled_ranges)
• MutationType::Discrete - Rounded-to-integer values with uniform selection (like ListGenotype).
  • Mutations ignore current value - all rounded-to-integer in range equally likely
  • Range 0.0..=4.0 yields values: 0.0, 1.0, 2.0, 3.0, 4.0 (with equal probability)
  • Useful for encoding: enums (0.0..=4.0), booleans (0.0..=1.0), or discrete choices
  • Neighbours and permutations include all integer values in the allele range

For backward compatibility, mutation type per gene is auto-detected from provided ranges in the following order (as it was before, implicitly):

• Has allele_mutation_scaled_ranges → scaled for all genes
• Has allele_mutation_ranges → relative for all genes
• Else → random for all genes

Explicit .with_mutation_types() overrides any auto-detected mutation range settings.

Added

• Add optional .with_mutation_types() to MultiRangeGenotype builder

Changed

• Move .mutation_type() from Genotype Trait to individual genotype implementations, as it is no longer a single uniform value for all genotypes.

[0.21.0] - 2025-10-06

Design choices and internal impact (API changes listed separately below)

The primary goal was easier custom Mutate and Crossover implementations, which would require a high level of Genotype unification and simplification. This was mostly blocked by the GPU optimization premise provided by the centralized DynamicMatrixGenotype and StaticMatrixGenotype with all their added centralized complexity (ChromosomeManager and GenesPointer v. GenesOwner etc...).

The first attempt was to split the library in two: centralized v. distributed, where the distributed track could become less genotype-heavy and more flexible. But it also happens the GPU zero-copy optimization premise was flawed as memory transfers are required regardless of pre-transfer layout. So on the end we just dropped the whole centralized approach and archived it in archive/centralized-gpu-experiment branch for later use, when zero-copy actually becomes viable.

Now the library is restructured to a simpler form, moving a lot of responsibilities away from Genotype which was becoming too heavy and centralized: All genes are now Vec<Allele> and owned by the Chromosome (which now only has one implementation, no genotype specific variants anymore).

Chromosome recycling has been moved from the Genotype (ChromosomeManager) to the Population, the enabling flag is on Genotype. So when making custom implementations remember to use the population's new_chromosome(), drop_chromomsome(), truncate(), truncate_external() & extend_from_within() methods for the chromosomes. Or just disable the recycling (no risk of errors).

However, Genotype unification still proved impossible - each type has fundamentally different requirements. So the best route to allow for easier custom Mutate and Crossover implementations, was to make them user-genotype specific using an associated type Genotype on Mutate and Crossover traits (following existing Fitness pattern). The Genotypes now also have some implementation-specific helper methods to support custom implementations: sample_allele(), sample_gene_delta(), sample_gene_index(), sample_gene_indices().

Skip the associated type Genotype on Select and Extension for now, as these mainly work with the chromosome metadata and are not genotype specific.

General usage by client has little impact, most is internal.

API Impact

• Replace BinaryChromosome with Chromosome<bool>
• Remove all other Chromosome struct genotype specific prefixes (e.g. ListChromosome<..> -> Chromosome<..>)
• Use genetic_algorithm::impl_allele!(CustomAllele); to implement Allele for your CustomAllele

Changed

• Add associated type Genotype to Mutate and Crossover traits (following existing Fitness pattern).
• Move chromosome recycling from ChromosomeManager to Population
• Move genes hashing from the Genotype to Chromosome making the Allele trait responsible for the hashing implementation (with impl_allele! macro for default implementation)
• Moved current_scale_index from StrategyState to Genotype. This is the only change adding Genotype logic. The scaling is only implemented by RangeGenotype and MultiRangeGenotype, so it felt more genotype specific. It does make the Genotype mutable again, which is an accepted tradeoff.

Caution

Remember to .reset_scale_index() when reusing your genotype with a custom implementation of multiple runs! (the library's implementation for multiple runs, e.g. call_repeatedly(), clones the builder and then casts it to the specific strategy, so each run starts with a cloned unused genotype)

Removed

• Remove matrix genotypes (DynamicMatrixGenotype, StaticMatrixGenotype)
• Remove ChromosomeManager trait
• Remove GenesOwner and GenesPointer traits
• Remove BitGenotype - use BinaryGenotype with Vec<bool> instead
• Remove calculate_for_population() as the population-level fitness calculation user hook. All is now calculate_for_chromosome() which can now be compile time guarded

[0.20.5] - 2025-05-21

Added

• Add PermutableGenotype support for scaled RangeGenotype and scaled MultiRangeGenotype. This approach implements a increasingly localized grid search with increasing precision using the allele_mutation_scaled_range(s) to define the search scope and grid steps
  • First scale (index = 0) traverses the whole allele_range(s) with the upper bound of the first scale as step size.
  • Other scales (index > 0) center around the best chromosome of the previous scale, traversing the previous scale bounds around the best chromosome with the upper bound of the current scale as step size.
  • Scale down and repeat after grid is fully traversed
  • Note: Not implemented for relative or random mutation types
  • Note: Not implemented for DynamicMatrixGenotype and StaticMatrixGenotype, as matrix has no use in permutation
  • Note: Scales should be symmetrical around zero, as always, but good to remember
  • Note: As an added benefit the generic StrategyBuilder with deferred specialization can now also be used for RangeGenotype and MultiRangeGenotype
• Keep track of scale_generation next to current_generation in StrategyState, resets every scale increment, no use yet
• Add mutation_type_allows_permutation() guard on Genotype and check in Permutate strategy builder

Changed

• Add chromosome and scale_index parameters to PermutateGenotype chromosome_permutations_into_iter() function, ignore for all existing implementations, used by RangeGenotype and MultiRangeGenotype

[0.20.4] - 2025-05-15

Fixed

• The replacement_rate in selection was not working, as the age was incremented before selection leading to only parents and no offspring during selection. Effectively the whole population was selected against without partitioninig. Fix by moving the age increment from the start of the new evolve generation (before selection) to just before crossover (after selection and after extension, as extension is form of selection where offspring information might still be relevant as well).
• For GenotypeMultiRange, remove the weighted probability of mutating a gene, depending on its allele_range size. The assumption of weighted mutation probability holds for GenotypeMultiList and GenotypeMultiUnique, in order to avoid over mutating the smaller sets with regard to the larger sets. But it does not for GenotypeMultiRange. We cannot make the assumption that the range size says anything about the search space size. Now probability of mutating a gene is uniform, regardless of its allele_range size in GenotypeMultiRange.

Changed

• MutateMultiGene and MutateMultiGeneDynamic now avoid mutating the same genes twice
• MutateMultiGene and MutateMultiGeneDynamic now mutate the exact number_of_mutations times if mutation is occuring

Added

• Add MutateMultiGeneRange to provide the previous MutateMultiGene behaviour. This samples the number_of_mutations from the provided number_of_mutations_range. And it allows for duplicate mutations of the same gene (as it is less strict anyway)
• Add parents and offspring size to on_new_generation reporting of EvolveReporterSimple

[0.20.3] - 2025-05-14

Added

• Initialize first HillClimb SteepestAscent population with the seed_genes_list, to ensure the best seed is taken as the starting point when seeds are provided.

[0.20.2] - 2025-05-12

Added

• Add max_generations ending condition on Evolve and HillClimb strategies (also blocked by optional valid_fitness_score, as max_stale_generations is)
• Add ExtensionMassDeduplication which requires with_genes_hashing(true) on the Genotype (otherwise ignored)
• Add Population unique_chromosome_indices() and best_unique_chromosome_indices() support functions

Changed

• Nuance ExtensionMassGenesis trying to use unique Adam en Eve Chromosomes when genes_hashes are availble, otherwise just take 2 best chromosomes (possibly duplicates)
• Decided not to use unique best chromosomes for the elitism_rate in ExtensionMassDegeneration and ExtensionMassExtinction. Just use the best chromosomes (possibly duplicates), as uniqueness hard-limits the amount of selected elites in the typical low cardinality situation, which seems unwanted behaviour

[0.20.1] - 2025-05-08

Added

• Add elitism_rate to ExtensionMassExtinction and ExtensionMassDegeneration. The elitism_rate ensures the passing of the best chromosomes before extinction or degeneration is applied

Fixed

• Fix best_chromosome_indices() on Population in case where there are no fitness values other than None

[0.20.0] - 2025-05-08

Changed

• Completely redo Selection in order to align with general genetic-algorithm terminology:
  • replacement_rate: the target fraction of the population which exists of children. Generational Replacement and Steady-State Replacement can both be modelled with this parameter by setting it respectively to 1.0 and 0.2-0.8. High values converge faster, but risk losing good solutions. Low values convergence slower. If there is a shortage of population after the ideal fraction, firstly remaining non-selected children and secondly remaining non-selected parents will be used to fill the shortage to avoid population collapse.
  • elitism_rate: a non-generational elite gate, which ensures passing of the best chromosomes before selection and replacement takes place. Value should typically be very low, between 0.01 and 0.05. Relevant for SelectTournament where the best chromosome is not guaranteed to be selected for a tournament if the population_size is larger than the target_population_size
• Completely redo Crossover in order to align with general genetic-algorithm terminology:
  • selection_rate: the fraction of parents which are selected for reproduction. This selection adds offspring to the population, the other parents do not. The population now grows by the added offspring, as the parents are not replaced yet. Value should typically be between 0.4 and 0.8. High values risk of premature convergence. Low values reduce diversity if overused.
  • crossover_rate (or recombination-rate): the fraction of selected parents to crossover, the remaining parents just clone as offspring. Value should typically be between 0.5 and 0.8. High values converge faster, but risk losing good solutions. Low values have poor exploration and risk of premature convergence
• Note that max_chromosome_age is implemented at the EvolveBuilder level, it will remove old chromosomes in the evolve loop before selection. The elitism_rate will not save them.
• Note that population_size now grows with added offspring during crossover and no longer replaces parents in-place with children. This means the StaticMatrixGenotype needs to reserve extra population space beyond the target_population_size
• Note that in previous releases all parents had an in-place crossover. The behaviour from previous releases can be achieved by setting the replacement_rate to 1.0 (drop parents), the selection_rate to 1.0 (all crossover) and the crossover_rate to 1.0 (all crossover). The new release is a bit slower as in-place crossover was faster than keeping the parents around and dropping them during selection
• Rename and refactor some internal Chromosome recycling methods

Added

• Add best_chromosome_indices() on Population, used to implement elitism_rate without the need for a full sort
• Add CrossoverRejuvenate as new limited cloning implementation for the old CrossoverClone behaviour, which had limited cloning. The new CrossoverClone now adds actual clones as offspring without removing the parents, which is much more cloning than the original. For CrossoverRejuvenate: drop non-selected parents, then clone top parents to repopulate, then rejuvenate selected parents to children in place. No copying of chromosomes for creating the offspring itself, only for repopulating the dropped non-selected parents (smaller fraction). However the cloning of top-parents is crucial for driving the Evolve process as experimenting with the evolve_nqueens example demonstrated.

Removed

• Remove reference_id: usize from Chromosome as user controlled alternative to genes_hash, as genes_hash is now formally supported for all types.

[0.19.4] - 2025-05-05

Added

• Add with_fitness_cache(size) builder step to the HillClimbBuider as well. There are some use cases with long tails and replace_on_equal_fitness(true) where caching information is useful.
• Add FitnessGenes<Self> type alias for better fitness API (next to FitnessChromosome<Self> etc.)

[0.19.3] - 2025-04-15

Fixed

• Forgot to calculate_genes_hash() after chromosome_constructor_genes(), refactor a bunch to ensure this never happens again.

[0.19.2] - 2025-04-15

Changed

• Cycle through the seed_genes_list to fill the initial population for Evolve strategy (instead of random sampling from the seed genes). This is done to ensure all seed genes reach the initial population (if the target_population_size is larger than the seed_genes_list). The HillClimb strategy still samples a single random starting seed gene as the starting point for each run (not cycling through them in repeated runs)

[0.19.1] - 2025-04-15

Changed

• Silently ignore zero cache size in with_fitness_cache(size), to support superset builder strategies where cache size is derived from unset parameters (defaulting to zero)

[0.19.0] - 2025-04-15

Added

• Add with_fitness_cache(size) builder step to the EvolveBuilder. When applying this step, a thread-safe FitnessCache object is stored on the EvolveConfig which manages an Arc-wrapped LRU cache of fitness values for genes.
  • Note that caching only works when the genes_hash is stored on chromosome as well (through the with_genes_hashing() builder step), as this is the cache key.
  • Note the FitnessCache is stored on EvolveConfig, not EvolveState, as the cache is external to the strategy (and reused over multiple repeated runs).
  • Note that caching is only useful for long stale runs, but it is better to avoid those in general. This makes the cache hit/miss reported in the EvolveReporterSimple more of a hint where the hyperparameters should be adjusted to increase population diversity. I don't think caching is the proper solution to ovelry revisiting the same genes. Keeping the feature for now though, as the hint is valuable in itself.
  • Note that +0.0 and -0.0 hash differently on floats when using with_genes_hashing().
  • Decided not to support the fitness cache in the Permutate and HillClimb strategies as these should (almost) never revisit the same genes anyway.

Changed

• Update pprof to v0.14.0 due to security issue on v0.13.0
• Change some internal Fitness trait method parameters as the StrategyConfig and FitnessCache need to be passed around.

[0.18.1] - 2025-01-13

Changed

• Use rustc_hash::FxHasher instead of DefaultHasher for 2x-5x faster genes hashing

[0.18.0] - 2025-01-12

Added

• Option to store genes_hash on Chromosome by setting with_genes_hashing(true) on the Genotype. This can be used for better population cardinality estimation (with respect to the default fitness score based estimate), but has a relatively high overhead on the main Evolve loop (mostly noticable in Crossover duration).
• Store population_cardinality on EvolveState, using genes_hash cardinality if set, otherwise fallback to fitness score cardinality. Update the population_cardinality just after selection in the Evolve loop and use for rest of the generation. Crossover only extends existing cardinality if present. Mutation always adds cardinality, but might also all be dropped in selection again. So using the cardinality at the beginning of the Evolve loop is a good enough estimate

Changed

• No longer count Nones as unique fitness score cardinality. Instead make fitness_score_cardinality() return None of no values can be used to estimating the cardinality (ensuring we keep avoiding the immediate Extension trigger at the start of the iteration)
• Make MutateSingleGeneDynamic, MutateMultiGeneDynamic, ExtensionMassGenesis, ExtensionMassExtinction and ExtensionMassDegeneration use EvolveState population_cardinality as guard check

Dropped

• Remove old GenesKey type and genes_key() function, replaced by GenesHash
• Disallow f32 and f64 as Allele for List and Unique genotypes by requiring the Allele to implement Hash for those Genotypes (including tuple Alleles). Now we can use standard Hashing for normal Alleles and use bytemuck Hashing for RangeAllele. This allows for easier implementation of genes based cardinality

[0.17.1] - 2024-10-10

Changed

• Make MutateMultiGene and MutateMultiGeneDynamic mutate up to the provided number_of_mutations (instead of always the exact amount of mutations)

[0.17.0] - 2024-10-04

Added

• Add on_exit() event to StrategyReporter
• Add StrategyVariant info in on_enter() and on_exit() events
• Add fitness_duration_rate() to StrategyState and use in StrategyReporter

Changed

• Permutation with seed_genes_list now only permutates over the seeded genes (useful to calculate only a specific predefined set)
• Add cleanup step in strategies. As the repeated/speciated calls keep the runs around, it seems better to cleanup the population and genotype storage
• Rename StrategyAction::Init to StrategyAction::SetupAndCleanup
• Rename on_init() event to on_enter() in StrategyReporter
• Move duration reporting to on_exit() in reporters to include the cleanup duration

[0.16.0] - 2024-09-18

Changed

• Add a buffered option to all Reporters (through new_with_buffer())
  • When the buffer is off (default), the reporter will print to stdout
  • When the buffer is on, the reporter will print to the internal buffer, which can be read out through flush_reporter() on the strategy later
• Change call_repeatedly(), call_par_repeatedly(), call_speciated() and call_par_speciated() to return a tuple (best_run, other_runs). This way the reporter buffers of the other runs can be read out as well afterwards

Dropped

• Drop StrategyReporterBuffer, as all reporters now have a buffered option

[0.15.1] - 2024-09-17

Added

• Add StrategyReporterBuffer implementation with an internal buffer instead of stdout
• Add flush_reporter() in Strategy as the strategy can be boxed and we need a way to get to the buffer of the Reporter

[0.15.0] - 2024-09-17

Changed

• Implement StrategyReporter trait
  • It was held off in previous releases, because of the generics in the API, but it is needed for the superset StrategyBuilder
  • Provide StrategyReporterNoop, StrategyReporterDuration and StrategyReporterSimple reporters, usable by all strategies (but less informative)
  • Re-export StrategyReporterNoop as EvolveReporterNoop, HillClimbReporterNoop and PermutateReporterNoop
  • Re-export StrategyReporterDuration as EvolveReporterDuration, HillClimbReporterDuration and PermutateReporterDuration
  • Re-implement EvolveReporterSimple, HillClimbReporterSimple, PermutateReporterSimple as strategy specialized versions
• Implement EvolveGenotype for all genotypes (as it was implicit) and move crossover functions over from Genotype
• Rename IncrementalGenotype to HillClimbGenotype
• Rename PermutableGenotype to PermutateGenotype
• Move Extension step to follow after Select step, so the fitness_score_cardinality of the selected population can be taken as a trigger
• Move reporter event on_new_generation to after selection for Evolve, as it is a more informative location in the loop

Added

• Add StrategyBuilder, a superset builder which delays specialization to Evolve, HillClimb or Permutate. Only usable by genotypes that implement all strategies (i.e. not for RangeGenotype and friends)
• Add call(), call_repeatedly(), call_par_repeatedly(), call_speciated() and call_par_speciated() to StrategyBuilder (fallback to equivalent of not available on specialized strategy)
• Add EvolveVariant (Standard only) and PermutateVariant (Standard only) for symmetry next to HillClimbVariant
• Implement mutation_type() for all genotypes. Use it to trigger scaling logic in Evolve and HillClimb.
• Implement MutationType::Random in fill_neighbouring_population() for RangeGenotype, MultiRangeGenotype, DynamicMatrixGenotype and StaticMatrixGenotype. It is not advised to use in HillClimb context, but a panic is worse.

Dropped

• Drop EvolveReporterLog, HillClimbReporterLog and PermutateReporterLog

[0.14.0] - 2024-09-12

Design choices and internal impact (API changes listed separately below)

• In order to support future GPU acceleration, the possibilty for the Genotype to store the genes of the whole population in a single contiguous memory location has been added. This has the following effects:
  • The Genotype has to be mutable and passed along for most operations. Affects a lot of interal function paramters
  • Chromosomes no longer always own their own genes, but can also just point to a section of the genotype's data storage. New triats GenesOwner (has genes) and GenesPointer (has row_id) for chromosomes
  • Each Genotype now has its own type of Chromosome, which implements the Genotype's genes storage model. Genotypes get an associated Chromosome type, all with their own alias. This leads to three core chromosome implementations:
    • VectorChromosome, stores Vec<Allele> in genes field (the original chromosome)
    • BitChromosome, stores FixedBitSet in genes field
    • RowChromosome, stores genotype data row in row_id fields
  • Chromosomes can't just be created, cloned and dropped, as the genotype needs to keep track of which sections of the data storage are in use. Therefore Genotype now is the constructor and destructor of chromosomes. Added ChromosomeManager trait to implement on the Genotoype. The strategies and plugins now need to properly call population reduction and regrow methods through this ChromosomeManager trait
  • The Fitness now has two implementation points, the normal calculate_for_chromosome(...) and a population level calculate_for_population(...). The latter is optional and can be used to calculate the genotype data strucure as a whole.
  • Because the chromosomes no longer always hold the genes, returning a best_chromosome() as end result from the strategies is not really suitable anymore. The method still is available when using standard chromosomes, but the new best_genes_and_fitness_score() is the preferred method as it works for all chromosome types.
  • The best_genes are now stored on the Genotype, instead of in a chromosome clone on the StrategyState. The latter would require the Genotype internal storage to keep a row reserved for the best_chromosome clone, which isn't nice.
• Because the Genotype now constructs and destructs chromosomes, this feature can be leveraged to recycle chromosome allocations for all Genotypes. This leads to an overal performance improvement, especially noticable for large genes sizes and low survival rates.

Changed (API)

• The calculate_for_chromosome(...) client implementation now gets a reference to Genotype, which can subsequently be ignored for standard use.
• The EvolveReporter, HillClimbReporter and PermutateReporter traits now also get a reference to Genotype on all functions, for the same reason
• Add FitnessGenotype<Self>, FitnessChromosome<Self> & FitnessPopulation<Self> type aliases for better fitness API

Added (API)

• Add DynamicMatrixGenotype, storing the population's genes in a single contiguous memory Vec<Allele> on the heap. All other features are like RangeGenotype.
• Add StaticMatrixGenotype, storing the population's genes with a single contiguous memory Box<[[T; N]; M]> on the heap. All other features are like RangeGenotype. N is the genes size and M is the population size:
  • For Evolve, M would be the target_population_size
  • For HillClimbVariant::SteepesAscent, M would be the neighbouring_population_size
• Add calculate_for_population(...) client implementation option in Fitness, only usable for the new matrix genotypes above.
• Add best_genes_and_fitness_score() function on Evolve, HillClimb and Permutate. Prefer this use over best_chromosome().
• Add utility methods genes_slice(chromosome) and best_genes_slice() to Genotype, returning the genes as a slice for all chromosome types (GenesOwned or GenesPointer)
• Add Fitness placeholder SumDynamicMatrix for DynamicMatrixGenotype (with optional precision)
• Add Fitness placeholder SumStaticMatrix for StaticMatrixGenotype (with optional precision)
• Add EvolveReporterDuration, HillClimbReporterDuration and PermutateReporterDuration to report only on the duration of the different phases of the strategies

Removed (API)

• Drop HillClimbVariant::StochasticSecondary and HillClimbVariant::SteepestAscentSecondary as call_repeatedly(...) on the basic variant is much more efficient
• Drop CrossoverParMultiPoint as it conflicts with storage owning Genotypes. You would have to provide a mutable Genotype in a Mutex, which is not worth the effort

[0.13.0] - 2024-09-11

Changed

• Rename Compete to Select
• Redo Select/Crossover selection & survival rates:
  • Remove Crossover parent_survival_rate and add Select selection_rate
  • Select now reduces the population and Crossover restores the population with the best parents after creating new offspring
  • Simulate the old behaviour of keeping all the parents by setting the selection_rate = 0.5 and doubling the target_population_size
• Implement Allele for () and set BitGenotype::Allele = () as it is not used

Removed

• Drop BinaryAllele type alias for bool. It is the only of it's kind and never used

Fixed

• Fix SelectElite sorting (should be best first, was best last). Effect was that the best part of population was dropped instead of kept, resulting in slow to no solution in Evolve

[0.12.1] - 2024-09-07

Fixed

• Fix CompeteElite sorting (should be best first, was best last). Effect was that the best part of population was dropped instead of kept, resulting in slow to no solution in Evolve

[0.12.0] - 2024-09-03

This is a major breaking release (back to pre-v0.9.0 API), see Changed:

Changed

• Add formal Genes trait: Genes: Clone + Send + Sync + Debug
• Change associated type from Allele to Genotype for: Fitness, EvolveReporter, HillClimbReporter and PermutateReporter
• Change generic type Allele to Genotype for: Chromosome, Population and other structs/functions using these types
• Store Genotype::Genes instead of Vec<Genotype::Allele> in the Chromosome genes field

Addded

• Allow for non-Vec based genes in Genotype. Most existing Genotype implementations use Vec<Allele> as genes, but now alternatives are possible
• Add BitGenotype using FixedBitSet for genes storage. Functionally the same as BinaryGenotype, but better for large genes sizes as storage is much more efficient than Vec<bool>.
• Add Fitness placeholder CountOnes for BitGenotype

[0.11.1] - 2024-09-07

Fixed

• Fix CompeteElite sorting (should be best first, was best last). Effect was that the best part of population was dropped instead of kept, resulting in slow to no solution in Evolve

[0.11.0] - 2024-09-02

Changed

• Change Crossover's keep_parent parameter to parent_survival_rate. This keeps a fraction of the top parents, instead of the previous all or nothing boolean.
• Add duration tracking of interal actions for Evolve, HillClimb & Permutate
• Add duration tracking results in EvolveReporterSimple, HillClimbReporterSimple and PermutateReporterSimple
• Remove Genotype parameter from EvolveReporter, HillClimbReporter and PermutateReporter on_start event, use on_init event for reporting about Genotype

Removed

• Drop ExtensionMassInvasion as reseeding the population conflicts with scaling. And reseeding can better be done by call_repeatedly or call_speciated anyway

Fixed

• Remove yanked package warning for bytemuck v1.16.1 in Cargo.lock by updating all dependencies to latest versions

[0.10.3] - 2024-08-29

Added

• Add performance considerations in documentation
• Add CrossoverMultiGene, CrossoverMultiPoint and CrossoverParMultiPoint
• Add Fitness placeholders Countdown and CountdownNoisy

Changed

• Move all crossover and mutation logic to Genotype. The goal is to limit the knowledge of the internal genes structure to the Genotype only. Lots of internal changes, not relevant for API.
• Improve CrossoverSinglePoint and CrossoverMultiPoint performance by avoiding cloning of genes
• Reimplement CrossoverUniform as CrossoverMultiGene with number_of_crossovers = genes_size / 2 and allow_duplicates = true, no API change

Removed

• Drop CrossoverParUniform in favor of CrossoverParMultiPoint as a more useful example implementation, although parallel execution of crossovers have no performance benefits for most situations

[0.10.2] - 2024-08-26

Added

• Add with_rng_seed_from_u64_option() function to EvolveBuilder and HillClimbBuider for more flexible API

[0.10.1] - 2024-08-26

Changed

• Move PartialEq requirement from general Allele to ListGenotype and MultiListGenotype specific allele requirements

Added

• Implement Allele trait for tuple sizes 1 to 12, as 12 is the limit for PartialEq in tuples

Removed

• Remove Default requirement on Allele for RangeGenotype and MultiRangeGenotype (no longer used)
• Remove Zero requirement on Allele for RangeGenotype and MultiRangeGenotype (no longer used)

[0.10.0] - 2024-08-26

Changed

• Add Copy to Allele trait, this drops support for String Alleles, but is fine for primitives, enum and structs
• Make the randomness provider internal to the API. You no longer need to provide it in the call() methods
• Add with_rng_seed_from_u64() functions to EvolveBuilder and HillClimbBuider for reproducible runs (e.g. testing)
• Align all multithreading approaches of Evolve, HillClimb & Permutate using rayon::iter and std::sync::mpsc
• Distinguish between internal and external multithreading:
  • Internal multithreading means: parallel execution within an Evolve, HillClimb or Permutate run (mainly Fitness calculations)
  • External multithreading means: parallel execution of multiple independent Evolve or HillClimb runs.
  • Note that Permutate only has internal multithreading as repeated calls make no sense
  • Note that internal and external multithreading can be combined
  • Note that internal multithreading has been explored for Compete, Crossover and Mutate. But the overhead of parallel execution was too high, generally resulting in degradation of performance. The breakeven point was found only for huge populations or genes_sizes, and only where each gene was part of the calculation (e.g. CrossoverUniform). Since Fitness is a client implementation which could be very heavy depending on the domain, an explicit with_par_fitness() is used for enabling internal multithreading of the fitness calculation only. Adding with_par_crossover() and friends has been considered, but due to the little expected gain, separate implementations where possibly beneficial are added instead (e.g. CrossoverParUniform).
• Rename with_multithreading to with_par_fitness(), as to properly reflect it's effects only in the fitness calculations (internal multithreading)
• Require Send + Sync to Compete, Crossover, Extension and Mutate
• Change chromosome_permutations_into_iter() return type from Box<dyn Iterator> to impl Iterator
• Rename with_multithreading() to explicit with_par_fitness() for clarity of the effect

Added

• Add call_par_repeatedly() and call_par_speciated() to EvolveBuilder (external multithreading)
• Add call_par_repeatedly() to HillClimbBuilder (external multithreading)
• Add short-circuit for call_speciated() and call_par_speciated() when target_fitness_score is reached during speciation
• Add CountTrueWithSleep fitness placeholder for use in multithreading examples and benchmarking
• Add CrossoverParUniform for a multithreaded implemenation of CrossoverUniform
• Add reference_id: usize to Chromosome as user controlled alternative to genes_key() for the GPU calculation use case described in issue 5

[0.9.0] - 2024-08-20

This is a major breaking release, see Changed:

Changed

• Add formal Allele trait: Allele: Clone + Send + Sync + PartialEq + Debug
• Change associated type from Genotype to Allele for: Fitness, EvolveReporter, HillClimbReporter and PermutateReporter
• Change generic type Genotype to Allele for: Chromosome, Population and other structs/functions using these types
• Rename DiscreteGenotype to ListGenotype (incl. Multi)
• Rename ContinuousGenotype to RangeGenotype (incl Multi)
• Generalize RangeGenotype for numeric types (incl. Multi, default still f32, but other float and integer types are now supported)
• Replace Range with RangeInclusive for all ranges in RangeGenotype in order to handle integer ranges more intuitively (incl. Multi)
• Change Fitness placeholders SumContinuousAllele and SumDiscreteAllele to generalized SumGenes (with optional precision)
• Reimplement scaling completely now RangeGenotype is generalized.
  • Drop f32 Scaling logic
  • Set allele_mutation_scaled_range on RangeGenotype to define scaling (incl. Multi) instead of with_scaling() in HillClimb build step
  • Mutation distance only on edges of current scale (e.g. -1 and +1 for -1..-1 scale)
  • Scale down after max_stale_generations is reached and reset new stale_generations counter to zero
  • Only trigger max_stale_generations ending condition when already reached the smallest scale
• How to mutate now fully controlled by Genotype with random, relative or scaled mutations options (relative and scaled only possible for RangeGenotype, incl. Multi)
  • Rename MutateSingleGeneRandom to MutateSingleGene as it just calls mutate_chromosome() on Genotype
  • Rename MutateSingleGeneRandomDynamic to MutateSingleGeneDynamic as it just calls mutate_chromosome() on Genotype
  • Rename MutateMultiGeneRandom to MutateMultiGene as it just calls mutate_chromosome() on Genotype
  • Rename MutateMultiGeneRandomDynamic to MutateMultiGeneDynamicas it just calls mutate_chromosome() on Genotype
  • Rename allele_neighbour_range to allele_mutation_range in RangeGenotype (incl. Multi) to define relative mutation
  • Add allele_mutation_scaled_range to RangeGenotype (incl. Multi) to define scaled mutation
• All changes to RangeGenotype are reflected in MultiRangeGenotype as well

Added

• Allow relative mutations for Evolve as well, as it is a Genotype responsibility now
• Allow scaled mutations for Evolve as well, as it is a Genotype responsibility now
  • Scale down after max_stale_generations is reached and reset stale_generations to zero
  • Only trigger max_stale_generations ending condition when already reached the smallest scale
• Add replace_on_equal_fitness to builders to allow for lateral moves in search space
  • Evolve: defaults to false, maybe useful to avoid repeatedly seeding with the same best chromosomes after mass extinction events
  • HillClimb: defaults to true, crucial for some type of problems with discrete fitness steps like nqueens
  • Permutate: defaults to false, makes no sense to use in this strategy

Removed

• Drop MutateSingleGeneDistance as random, relative or scaled mutations are now handled by Genotype and not the caller

[0.8.2] - 2024-08-09

Added

• Improve bootstrapped reporter outputs EvolveReporterSimple, HillClimbReporterSimple and PermutateReporterSimple
• Implement ExtensionNoop default for EvolveBuilder and remove now optional with_extension() steps in examples
• Implement EvolveReporterNoop default for EvolveBuilder and remove now optional with_reporter() steps in examples
• Implement HillClimbReporterNoop default for HillClimbBuilder and remove now optional with_reporter() steps in examples
• Implement PermutateReporterNoop default for PermutateBuilder and remove now optional with_reporter() steps in examples

Changed

• Align EvolveReporter, HillClimbReporter and PermutateReporter traits to take EvolveState and EvolveConfig as parameters in further aligment with Mutate, Compete, Crossover and Extension traits.
• Add Sync trait everywhere where Send trait was required.

Fixed

• Fix major issue where cardinality starts out as 0 as there are no fitness calculations yet. This triggers the optional extension event, if set, at the start of the evolve loop (killing seed population and diversity). Issue was introduced in v0.8.0 with fitness_score_cardinality(). Solve by adding None fitness counts to cardinality.

[0.8.1] - 2024-08-08

Added

• Always implement new() next to default(). Use new() in public API examples

Changed

• Rename new() to new_with_flags() for more verbose reporting in EvolveReporterSimple, HillClimbReporterSimple and PermutateReporterSimple
• Add simpler new() to only take period: usize and set all flags to false (as this is the sensible less noisy default) in EvolveReporterSimple, HillClimbReporterSimple and PermutateReporterSimple

[0.8.0] - 2024-08-07

Added

• Add PermutateConfig and PermutateState to align structure with Evolve and HillClimb
• Extract StrategyConfig trait and use for EvolveConfig, HillClimbConfig and PermutateConfig
• Extract StrategyState trait and use for EvolveState, HillClimbState and PermutateState
• Add pluggable EvolveReporter to Evolve strategy
  • Set in builder using with_reporter()
  • Custom implementations by client are encouraged, the API resembles the Fitness API
  • Add bootstrap implementations EvolveReporterNoop, EvolveReporterSimple and EvolveReporterLog
• Add pluggable HillClimbReporter to HillClimb strategy
  • Set in builder using with_reporter()
  • Custom implementations by client are encouraged, the API resembles the Fitness API
  • Add bootstrap implementations HillClimbReporterNoop, HillClimbReporterSimple and HillClimbReporterLog
• Add pluggable PermutateReporter to Permutate strategy
  • Set in builder using with_reporter()
  • Custom implementations by client are encouraged, the API resembles the Fitness API
  • Add bootstrap implementations PermutateReporterNoop, PermutateReporterSimple and PermutateReporterLog
• Add fitness_score_cardinality() to Population
• Add MutateMultiGeneRandomDynamic (generalize to any number of mutations)
• Add MutateSingleGeneDistance (only for ContinuousGenotype)

Removed

• Drop fitness_score_uniformity() and fitness_score_prevalence() from Population
• Drop MutateDynamicRounds

Changed

• Align Mutate, Compete, Crossover and Extension traits to take EvolveState, EvolveConfig, EvolveReporter as parameters
• Reimplement MutateOnce as MutateSingleGeneRandom
• Reimplement MutateTwice as MutateMultiGeneRandom (generalize to any number of mutations)
• Reimplement MutateDynamicOnce as MutateSingleGeneRandomDynamic (also fix InvalidProbabilty issue)
• Replace target_uniformity with target_cardinality in MutateSingleGeneRandomDynamic and MutateMultiGeneRandomDynamic as uniformity is ill defined
• Replace uniformity_threshold with cardinality_threshold in Extension implementations, as uniformity is ill defined
• Move permutation total_population_size from PermutateConfig to PermutateState, so progress can be reported on in PermutateReporterSimple
• Move env_logger dependency to dev-dependencies as this crate is a library, not an executable

Note

• Note that HillClimb scaling needs review as it doesn't feel right in its design approach. Possibly align with MutateSingleGeneDistance approach?
• Extract StrategyReporter trait, but don't use because of error E0658: associated type defaults are unstable. So for EvolveReporter, HillClimbReporter and PermutateReporter the trait is shadowed as if it is implemented

[0.7.2] - 2024-07-27

Added

• Add Wrappers instead of Dispatchers as they keep state, behaviour is the same using into() (e.g. MutateOnce::new(0.2).into())

Removed

• Extract Meta logic to separate crate genetic_algorithm_meta
• Phase out the Dispatchers as they are replaced by Wrappers

[0.7.1] - 2024-07-23

Changed

• MSRV bumped to 1.71.1
• Solve RUSTSEC-2021-0145

[0.7.0] - 2023-05-25

Added

• Add Mutate implementations:
  • MutateTwice, support some form of swap-like behaviour where UniqueGenotype doesn't match with the problem space
  • MutateDynamicOnce, increase mutation probability when population uniformity is above threshold and vice versa
  • MutateDynamicRounds, increase mutation rounds when population uniformity is above threshold and vice versa
• Add HillClimbVariant::StochasticSecondary and HillClimbVariant::SteepestAscentSecondary as well for the same reasons as MutateTwice
• Add call_speciated next to the existing call_repeatedly in EvolveBuilder. This runs multiple independent evolve strategies and then competes their best chromosomes as starting population against each other in one final evolve strategy
• Add Chromosome age and optional with_max_chromosome_age to EvolveBuilder. Filtering chromosomes past the maximum age from the next generation
• Add best_generation() and best_fitness_score() to Strategy, so client implementation can report and switch more easily over different strategies. Return zero for Permutate::best_generation() as there is no concept of best generation there
• Add Extension step to Evolve, adding with_extension to EvolveBuilder, with several implementations:
  • ExtensionNoop, for no extension
  • ExtensionMassExtinction, trigger mass extinction to allow for cambrian explosion (no competition for a while, which allows for more diversity)
  • ExtensionMassGenesis, like ExtensionMassExtinction, but only a pair of best chromosomes (adam and eve) are taken as the start for the next generations
  • ExtensionMassInvasion, like ExtensionMassExtinction, but replace extinct population with random population (respecting seed_genes if present)
  • ExtensionMassDegeneration, simulate cambrian explosion by apply several rounds of uncontrolled mutation directly
• Add Population::fitness_score_unformity() as measure for uniformity (fraction between 0 and 1). Use as triggers for MutateDynamic* and Extension
• Add dispatch From to Evolve plugins for use in MetaConfigBuilder, instead of manual wrapping (e.g. MutateOnce::new(0.2).into() instead of MutateDispatch(MutateOnce::new(0.2))

Changed

• Refactor Compete, Crossover and Mutate from tuple structs to struct, initialize with ::new(), because the structs now have some mutable internal properties (e.g. MutateDynamicOnce). Make all plugins mutable for consistency
• Split off internal config and state structs for Evolve and HillClimb, leave Permutate untouched weighing overkill v. symmetry different there
• Split off internal plugins for Evolve (i.e. Mutate/Crossover/Compete/Extension)
• Change seed_genes to seed_genes_list to allow for multiple seed genes taken randomly (used in call_speciated)
• Only mutate children in the Mutate step, in earlier versions parents and children were mutated equally
• Refactor Evolve population_size property to target_population_size, thus also replacing with_population_size with with_target_population_size
• Add env_logger::init() to all examples, so the RUST_LOG environment variable works as expected
• Change HillClimbBuilder::with_scaling parameter from tuple to struct Scaling

Removed

• Phase out the with_mass_degeneration in EvolveBuilder as it is replaced by ExtensionMassDegeneration

[0.6.0] - 2022-10-14

Changed

• Calculate initial chromosome fitness in HillClimb to lock in on original seed if present

Removed

• Remove random_chromosome_probability to HillClimb as it was hackish

[0.5.4] - 2022-10-14

Added

• Add valid_fitness_score to block ending conditions until met for Evolve and HillClimb strategies

[0.5.3] - 2022-10-14

Changed

• Tweak TRACE logging

[0.5.2] - 2022-10-14

Added

• Add env_logger and some INFO/DEBUG/TRACE logging

Changed

• Count generation zero based

[0.5.1] - 2022-09-10

Fixed

• Solve lock-in to single best chromosome in stale HillClimbVariant::SteepestAscent by shuffling chromosomes before taking best

[0.5.0] - 2022-07-07

Added

• Add IncrementalGenotype Trait with neighbouring chromosome implementations
• Implement IncrementalGenotype for all Genotypes
• Add allele_neighbour_range to ContinuousGenotype
• Add allele_neighbour_ranges for MultiContinuousGenotype
• Add HillClimbVariant::Stochastic and HillClimbVariant::SteepestAscent
• Add HillClimb scaling (for ContinuousGenotype & MultiContinuousGenotype) to scale down neighbours on each round and use as ending condition
• Add random_chromosome_probability to HillClimb to avoid local optima
• Add multithreading to Permutate (parallel processing of chromosome generator)
• Add multithreading to Evolve (fitness execution for population)
• Add multithreading to HillClimb (fitness execution for HillClimbVariant::SteepestAscent population only)
• Add call_repeatedly for EvolveBuilder and HillClimbBuilder
• Add examples/evolve_milp.rs
• Add examples/evolve_scrabble.rs
• Add examples/hill_climb_scrabble.rs
• Add examples/hill_climb_milp.rs
• Add examples/permutate_scrabble.rs

Changed

• Require IncrementalGenotype for HillClimb strategy
• Refactor allele_values to allele_list
• Refactor allele_multi_values to allele_lists
• Refactor allele_multi_range to allele_ranges
• Add median/mean/stddev to report_round in Evolve and HillClimb
• Add precision to SumContinuousGenotype and SumMultiContinuousGenotype placeholders for better handling of decimal changes on cast to isize

[0.4.1] - 2022-06-14

Documentation

• Use SPDX license in Cargo.toml as the existing LICENSE file (MIT) was marked as non-standard by crates.io
• Add Apache 2.0 license

[0.4.0] - 2022-06-14

Documentation

• Note degeneration_range use as case by case configuration

Added

• Add Strategy trait and implement for Evolve and Permutate
• Add HillClimb strategy for when crossover is impossible or inefficient
• Add MultiUniqueGenotype
• Add table_seating example (hill_climb and evolve)

Changed

• Move Evolve & Permutate to strategy module
• Remove Genotype::is_unique and Crossover::allow_unique_genotype methods
  • Replace with Genotype::crossover_indexes and Crossover::require_crossover_indexes
  • Replace with Genotype::crossover_points and Crossover::require_crossover_points
• Rename UniqueDiscreteGenotype to UniqueGenotype as it is discrete by definition
• Rename PermutableGenotype::allele_values to PermutableGenotype::allele_values_for_chromosome_permutations for clarity of purpose
• Hide Evolve and Permutate internal fields (to align with Strategy trait)

[0.3.1] - 2022-05-16

Fixed

• forgot to update version in Cargo.toml

[0.3.0] - 2022-05-16

Documentation

• Make proper distinction between gene and allele as in the book "Genetic Algorithms in Elixir"

Added

• Add option to call() from EvolveBuilder & PermutateBuilder directly
• Add Fitness::Zero placeholder

Changed

• Refactor Evolve & Permutate to call(&mut self, ...)
• Refactor Fitness, Crossover, Mutate & Compete to take mutable population reference
• Improve performance in Crossover when not keeping parents
• Rename gene_value* to allele_value*
• Rename gene_ranges to allele_multi_range for symmetry reasons with allele_multi_values
• Rename gene_size to genes_size as it is not the size of a gene
• Rename CrossoverSingle to CrossoverSingleGene
• Rename CrossoverRange to CrossoverSinglePoint
• Rename CrossoverAll to CrossoverUniform

Removed

• Drop SetGenotype as it is always better implemented using BinaryGenotype
• Cleanup examples

[0.2.0] - 2022-05-13

Added

• Add SetGenotype<T>, with examples/evolve_knapsack_set.rs and examples/permutate_knapsack_set.rs

Changed

• Refactor fitness placeholders to placeholders module to emphasize that production use is not intended
• Rename Fitness::call_for_chromosome() to Fitness::calculate_for_chromosome()
• Replaced PermutableGenotype::population_factory() with PermutableGenotype::chromosome_permutations_into_iter()
• Use PermutableGenotype::chromosome_permutations_into_iter() in Permutate::call() instead of fully instantiated population
• Rename PermutableGenotype::population_factory_size() to PermutableGenotype::chromosome_permutations_size()
• Use num::BigUint for PermutableGenotype::chromosome_permutations_size() as it overflows easily
• Rename existing examples/evove_knapsack_set.rs to examples/evolve_knapsack_discrete.rs to note is uses DiscreteGenotype<T>

Documentation

• Improve rustdocs, refer to docs.rs documentation from general README.md

[0.1.1] - 2022-05-11

Documentation

• Added rustdocs, refer to crate.io documentation from general README.md

[0.1.0] - 2022-05-10

Initial version