self-improve

Automated code improvement uses iterative research, planning, and execution cycles to evolve code quality. This process analyzes existing code, generates targeted changes, and tests them against performance metrics to select the best versions.

Tournament selection for code evolution compares multiple generated code changes against each other to determine the highest performing variant. This automated selection process identifies the best modifications based on predefined performance metrics.

Autonomous code evolution can refactor complex codebases by generating and testing different code changes automatically. The system analyzes the code, applies improvements iteratively, and selects the best modifications based on performance metrics.

Automating codebase evolution requires specialized agents for code analysis, planning, and execution, along with tools for managing worktrees. These dependencies handle the research, modification, and version control processes required for autonomous evolution.

Git integration in autonomous code evolution provides version control for tracking iterative code changes. The system uses Git to manage worktrees, execute code modifications, and maintain the history of evolutionary improvements throughout the research and execution cycle.

Automated code evolution requires multiple specialized agent dependencies and a managed environment for worktrees. It is best suited for complex codebases where performance metrics can clearly evaluate generated changes, rather than simple or highly subjective refactoring tasks.