OpenEvolve is an open-source evolutionary coding agent that uses large language models (LLMs) to iteratively improve programs. It implements a MAP-Elites quality-diversity algorithm with island-based evolution, supporting multiple LLM backends (OpenAI, Claude Code, etc.) and a cascade evaluation pattern. The system accepts a user-provided initial program and an evaluator script, then repeatedly mutates the program via LLM-generated SEARCH/REPLACE diffs or full rewrites, evaluates the fitness, and maintains a diverse population across feature dimensions. The core architecture is a library with a CLI entry point (`openevolve-run`), a high-level API (`run_evolution`, `evolve_function`, `evolve_code`), and a controller that orchestrates the evolution loop, prompt sampling, LLM inference, parallel evaluation, and database storage. The repository includes 20+ diverse examples ranging from algorithm optimization (AlgoTune, TSP, circle packing) to mathematical discovery (Heilbronn, kissing number) and hardware-specific kernel tuning (MLX Metal, MLIR). The codebase is well-structured with extensive unit and integration tests, typed configuration, and a modular design that separates concerns (evaluator, LLM, database, prompt templates). It is actively maintained with frequent commits and responsive issue triage.
Metaheuristics.jl is a Julia library for global optimization implementing various metaheuristic algorithms for single-objective, multi-objective, and combinatorial optimization problems. The library provides a modular architecture with clear separation between core functionality (state management, problem definitions), algorithm implementations (organized by optimization type), operators (selection, crossover, mutation), test problems, performance indicators, and decision-making methods. The primary workflow involves defining an objective function and search space, selecting/configuring an algorithm, and calling the optimize() function to find optimal solutions. The library depends on standard Julia packages and specialized libraries for distance calculations, distributions, and multi-criteria decision-making.
MTO-Platform (MToP) is a comprehensive MATLAB benchmarking platform for evolutionary multitasking (EMT). It provides a unified, extensible framework for defining optimization problems, implementing algorithms, and evaluating performance metrics. The platform includes over 50 multitask algorithms (e.g., MFEA, AT-MFEA, TRADE, SSLT), 200+ multitask problem cases (including CEC, WCCI, and real-world benchmarks), and 20+ performance metrics (IGD, HV, Spread, etc.). It supports single-task, multi-task, many-task, multi-objective, constrained, and competitive optimization scenarios. The system features both a graphical user interface (GUI) and a command-line interface, with parallel execution support and integration with external tools like IOHanalyzer and Python Gym environments for policy search. The architecture is modular: base classes (Problem, Algorithm, Metric) define consistent interfaces, and all components inherit from them. Utility modules (Individual, Initialization, Selection, Operator, Multi-objective sorting) are shared across algorithms, promoting code reuse. The codebase also includes real-world applications such as global trajectory optimization, optimal power flow, sensor coverage, and photovoltaic model parameter extraction. While primarily a research platform, it also serves as a library of reusable evolutionary computation components. The repository is actively maintained (v1.10, references to 2026) but exhibits some technical drift: use of deprecated MATLAB functions (textread), large vendored third-party code (SOM Toolbox), and binary .mat files that may cause compatibility issues. Code duplication exists in problem definitions and some algorithm utilities. Despite these issues, the platform is well-structured, extensively documented, and widely cited in the EMT literature.