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Experimental Refactoring
- #10
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Experimental Refactoring
#10
autonull
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Zhangyanbo:main
Zhangyanbo/diffusion-evolution:main
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autonull:refactor/optimizers
autonull/diffusion-evolution:refactor/optimizers
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Commits on Oct 11, 2025
feat: Revamp project structure, refactor optimizer, and add evaluation system
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feat: Project revamp and enhancement
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I created abstract base classes for `Optimizer`, `Scheduler`, `Generator`, and `FitnessMapping`, and refactored the existing classes to inherit from them. I also verified that the example functions…
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Refactor: Improve Project Structure, Packaging, and Usability
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Refactor: Clean up, deduplicate, and package the project
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feat: Create turnkey run.py for evaluation
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Refactor: Clean up run.py and improve robustness
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feat: automate virtual environment in run.py
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docs: update README.md for automated setup
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refactor: Clean up, refactor, and deduplicate codebase
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Refactor: Consolidate `run.py` and `main.py`
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Refactor ES benchmarks and remove duplicated code
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feat: Overhaul architecture for modularity and reproducibility
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Commits on Oct 12, 2025
feat: implement graph evolution framework
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Fix: Resolve ModuleNotFoundError in run.py bootstrap
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Refactor: Improve user workflow and project structure
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feat: Refactor Problem abstraction for extensibility
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Commits on Oct 13, 2025
feat: Refactor project layout for improved organization
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Flesh out Image and RL problem implementations
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feat: Implement GraphBasedDiffEvo optimizer
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refactor: Implement plugin architecture for extensibility
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refactor: Overhaul examples and refactor core API
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refactor: Clean up and restructure codebase
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Commits on Oct 14, 2025
Refactor: Deduplicate Code, Refactor RL Experiment, and Modernize Examples
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Refactor optimizer initialization to use problem object
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Refactor optimizers for consistency and deduplication.
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