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DEAP/deap

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Deap

Features

  • Benchmarking Platforms - Provides a platform for prototyping, logging, and analyzing evolutionary algorithm performance with statistics tracking.
  • Custom Evolutionary Components - Lets users create custom fitness functions, individual representations, and genetic operators from modular building blocks.
  • Custom Genomes - Creates a user-defined data structure to represent an individual solution in an evolutionary algorithm.
  • Evolutionary Algorithms - Provides a framework for defining custom genetic representations and operators to build tailored evolutionary algorithms.

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  • Genetic Algorithms - Provides a full-featured genetic algorithm library with selection, crossover, and mutation operators.
  • Genetic Operator Implementations - Provides a library of selection, crossover, and mutation operators for evolving populations.
  • Genetic Programming Toolkits - Ships a dedicated toolkit for evolving tree-based programs and symbolic expressions via genetic programming.
  • Tree-Based Genetic Programming - Supports tree-based representations and operators for evolving programs and symbolic expressions.
  • Operator Pipeline Frameworks - Defines evolution as a pipeline of composable operator objects chained together for the generation loop.
  • Rapid Prototyping Frameworks - Provides a modular framework for rapidly prototyping evolutionary algorithms with custom components.
  • Genetic Algorithms - Ships predefined algorithms for evolution strategies, genetic algorithms, and genetic programming.
  • Evolutionary Statistics Loggers - Records and reports population metrics like fitness mean and standard deviation during an evolution run.
  • Genetic Chromosome Types - Provides a flexible type system for defining custom chromosomes and individuals in evolutionary algorithms.
  • Custom Constraint Functions - Applies custom constraint functions during evolution to ensure generated individuals satisfy domain-specific rules or bounds.
  • Adaptive Mutation Strategies - Adapts the mutation step size or covariance matrix during evolution to improve convergence on real-valued problems.
  • Genetic Program Evolution - Represents solutions as tree structures and evolves them using crossover, mutation, and selection.
  • Genetic Program Trees - Represents programs as nested tree structures with typed nodes for genetic programming operations.
  • Swarm Optimization Algorithms - Moves a population of candidate solutions through the search space using velocity updates informed by personal and global best positions.
  • Artificial Ant Navigators - Evolves a control program that guides an agent to collect food tokens on a toroidal grid.
  • Binary String Optimizers - Evolves a population of binary strings toward a target using selection, crossover, and mutation operators.
  • Boolean Circuit Solvers - Evolves a program that correctly evaluates a truth table using genetic programming with boolean primitives.
  • Parallel Evaluators - Distributes fitness evaluations across multiple processors or nodes using multiprocessing or SCOOP.
  • Transparent Evolutionary Parallelizers - Distributes fitness evaluations across multiple cores or nodes transparently without changing algorithm code.
  • Evolutionary Penalty Functions - Applies penalty functions to fitness evaluations to steer populations away from invalid solutions.
  • Estimation of Distribution Algorithms - Supports estimation of distribution algorithms that learn and sample from probabilistic models of promising solutions.
  • Evolutionary Parallelism Frameworks - Implements transparent parallel evaluation of fitness functions across multiple processors or nodes.
  • State Checkpointing - Saves the entire evolutionary state to disk for exact resumption of runs.
  • Transparent Parallel Evaluation - Wraps fitness evaluation in a parallel map for transparent multiprocessing without changing algorithm logic.
  • Elite Solution Archives - Maintains a separate archive of elite individuals to preserve best solutions across generations.
  • Generation Metric Logbooks - Collects per-generation metrics into a structured log for real-time and post-hoc analysis.
  • Constraint Enforcement Decorators - Ships decorators that wrap fitness evaluation to enforce constraints and apply penalty functions.
  • Resumable State Restoration - Serializes the full evolutionary state to disk for exact resumption of runs.
  • Evolutionary Algorithms - Distributed evolutionary computation framework.
  • General Machine Learning - Framework for evolutionary algorithms.
  • Machine Learning Frameworks - Framework for evolutionary algorithms in Python.
  • Optimization - Evolutionary computation framework.
  • Perception and Machine Learning - Framework for distributed evolutionary algorithms.
  • Computation and Optimization - Framework for evolutionary computation and rapid prototyping.
    • 6,336 stars·1,157 forks·Python·lgpl-3.0·17 views

    Star history

    Star history chart for deap/deapStar history chart for deap/deap

    How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

    Projects sharing features with Deap

    These projects share indexed features with Deap. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
    • guofei9987/scikit-optguofei9987 avatar

      guofei9987/scikit-opt

      6,583View on GitHub↗

      scikit-opt is a Python optimization library and numerical framework designed to solve complex global optimization problems. It provides a suite of metaheuristic algorithms and tools for finding global minima or maxima of objective functions. The library implements a variety of nature-inspired and swarm intelligence algorithms, including Genetic Algorithms, Particle Swarm Optimization, Differential Evolution, Simulated Annealing, and Ant Colony Optimization. It includes specialized solvers for discrete combinatorial challenges, such as the Traveling Salesman Problem. The framework supports th

      Python
      View on GitHub↗6,583
    • epistasislab/tpotEpistasisLab avatar

      EpistasisLab/tpot

      10,050View on GitHub↗

      TPOT is a Python automated machine learning tool and pipeline framework. It automatically searches, selects, and tunes machine learning algorithms and hyperparameters to identify the most effective model architecture. The system utilizes genetic programming to optimize these pipelines through evolutionary algorithms. To accelerate the search process, it functions as a multi-core evaluator that runs parallel training workflows across multiple processor cores. The framework supports the definition of custom objective functions to optimize pipelines based on specific performance metrics.

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    • facebookresearch/nevergradfacebookresearch avatar

      facebookresearch/nevergrad

      4,151View on GitHub↗

      Nevergrad is a gradient-free optimization library and hyperparameter optimization framework designed to find the minimum of objective functions without using derivatives. It serves as an asynchronous optimization engine that decouples parameter suggestions from result reporting to support parallel function evaluations. The project specializes in multi-objective optimization to identify Pareto fronts for competing goals and provides a suite for benchmarking the performance and convergence of different optimization routines. It supports black-box system optimization, enabling the tuning of exte

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    • eriklindernoren/ml-from-scratcheriklindernoren avatar

      eriklindernoren/ML-From-Scratch

      31,918View on GitHub↗

      This project is an educational toolkit that provides implementations of fundamental machine learning algorithms built from scratch. By avoiding high-level library abstractions, it serves as a pedagogical reference for understanding the mathematical foundations and core mechanics of supervised learning, unsupervised learning, and reinforcement learning models. The repository distinguishes itself through a modular approach to model construction, allowing users to build custom neural networks by chaining independent functional blocks. It covers a wide range of techniques, including gradient-base

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    Frequently asked questions

    What are the main features of deap/deap?

    The main features of deap/deap are: Benchmarking Platforms, Custom Evolutionary Components, Custom Genomes, Evolutionary Algorithms, Genetic Algorithms, Genetic Operator Implementations, Genetic Programming Toolkits, Tree-Based Genetic Programming.

    Which projects share features with deap/deap?

    Projects with overlapping indexed features include: guofei9987/scikit-opt — scikit-opt is a Python optimization library and numerical framework designed to solve complex global optimization… epistasislab/tpot — TPOT is a Python automated machine learning tool and pipeline framework. It automatically searches, selects, and tunes… facebookresearch/nevergrad — Nevergrad is a gradient-free optimization library and hyperparameter optimization framework designed to find the… eriklindernoren/ml-from-scratch — This project is an educational toolkit that provides implementations of fundamental machine learning algorithms built… dmlc/xgboost — XGBoost is a distributed machine learning library for implementing scalable gradient boosting decision trees used for… catalyst-team/catalyst — Accelerated deep learning R&D.