inspyred: Bio-inspired Algorithms in Python

• inspyred – A framework for creating bio-inspired computational intelligence algorithms in Python.
  • Example
  • Requirements
  • License
  • Resources
  • Citing
  • Features
  • Credits
• Installation
  • Stable release
  • From sources
• Usage
• Overview
  • Bio-inspired Computation
  • Design Methodology
  • Installation
  • Getting Help
• Tutorial
  • The Rastrigin Function
    • The Generator
    • The Evaluator
    • The Evolutionary Computation
  • Evolving Polygons
    • The Generator
    • The Evaluator
    • The Bounder
    • The Observer
    • The Evolutionary Computation
  • Lunar Explorer
    • The Generator
    • The Evaluator
    • The Evolutionary Computation
• Examples
  • Standard Algorithms
    • Genetic Algorithm
    • Evolution Strategy
    • Simulated Annealing
    • Differential Evolution Algorithm
    • Estimation of Distribution Algorithm
    • Pareto Archived Evolution Strategy (PAES)
    • Nondominated Sorting Genetic Algorithm (NSGA-II)
    • Particle Swarm Optimization
    • Ant Colony Optimization
  • Customized Algorithms
    • Custom Evolutionary Computation
    • Custom Archiver
    • Custom Observer
    • Custom Replacer
    • Custom Selector
    • Custom Terminator
    • Custom Variator
  • Advanced Usage
    • Discrete Optimization
      • The Traveling Salesman Problem
      • The Knapsack Problem
    • Evaluating Individuals Concurrently
      • The multiprocessing Module
      • Parallel Python
    • Island Models
    • Replacement via Niching
• Recipes
  • Lexicographic Ordering
  • Constraint Selection
  • Meta-Evolutionary Computation
  • Micro-Evolutionary Computation
  • Network Migrator
• Library Reference
  • Evolutionary Computation
    • ec – Evolutionary computation framework
    • Bounder
    • DEA
      • DEA.evolve()
    • DiscreteBounder
    • EDA
      • EDA.evolve()
    • ES
      • ES.evolve()
    • Error
    • EvolutionExit
    • EvolutionaryComputation
      • EvolutionaryComputation.evolve()
    • GA
      • GA.evolve()
    • Individual
    • SA
      • SA.evolve()
    • emo – Evolutionary multiobjective optimization
    • NSGA2
      • NSGA2.evolve()
    • PAES
      • PAES.evolve()
    • Pareto
    • analysis – Optimization result analysis
    • allele_plot()
    • fitness_statistics()
    • generation_plot()
    • hypervolume()
    • utilities – Optimization utility functions
    • BoundedOrderedDict
    • Objectify
    • memoize()
    • Operators
      • archivers – Solution archival methods
      • adaptive_grid_archiver()
      • best_archiver()
      • default_archiver()
      • population_archiver()
      • evaluators – Fitness evaluation methods
      • evaluator()
      • parallel_evaluation_mp()
      • parallel_evaluation_pp()
      • generators – Solution generation methods
      • diversify
      • strategize()
      • migrators – Solution migration methods
      • MultiprocessingMigrator
      • default_migration()
      • observers – Algorithm monitoring methods
      • EmailObserver
      • archive_observer()
      • best_observer()
      • default_observer()
      • file_observer()
      • plot_observer()
      • population_observer()
      • stats_observer()
      • replacers – Survivor replacement methods
      • comma_replacement()
      • crowding_replacement()
      • default_replacement()
      • generational_replacement()
      • nsga_replacement()
      • paes_replacement()
      • plus_replacement()
      • random_replacement()
      • simulated_annealing_replacement()
      • steady_state_replacement()
      • truncation_replacement()
      • selectors – Parent selection methods
      • default_selection()
      • fitness_proportionate_selection()
      • rank_selection()
      • tournament_selection()
      • truncation_selection()
      • uniform_selection()
      • terminators – Algorithm termination methods
      • average_fitness_termination()
      • default_termination()
      • diversity_termination()
      • evaluation_termination()
      • generation_termination()
      • no_improvement_termination()
      • time_termination()
      • user_termination()
      • variators – Solution variation methods
      • arithmetic_crossover()
      • bit_flip_mutation()
      • blend_crossover()
      • crossover()
      • default_variation()
      • gaussian_mutation()
      • heuristic_crossover()
      • inversion_mutation()
      • laplace_crossover()
      • mutator()
      • n_point_crossover()
      • nonuniform_mutation()
      • partially_matched_crossover()
      • random_reset_mutation()
      • scramble_mutation()
      • simulated_binary_crossover()
      • uniform_crossover()
  • Swarm Intelligence
    • swarm – Swarm intelligence
    • ACS
    • PSO
    • TrailComponent
    • topologies – Swarm topologies
    • ring_topology()
    • star_topology()
  • Benchmark Problems
    • benchmarks – Benchmark optimization functions
    • Benchmark
      • Benchmark.evaluator()
      • Benchmark.generator()
    • Binary
      • Binary.evaluator()
      • Binary.generator()
    • Single-Objective Benchmarks
      • Ackley
      • Griewank
      • Rastrigin
      • Rosenbrock
      • Schwefel
      • Sphere
    • Multi-Objective Benchmarks
      • Kursawe
      • DTLZ1
      • DTLZ2
      • DTLZ3
      • DTLZ4
      • DTLZ5
      • DTLZ6
      • DTLZ7
    • Discrete Optimization Benchmarks
      • Knapsack
      • TSP
• Troubleshooting
  • Always Store the Seed
  • Use and Consult the Logs
  • Choose Operators that Work Together
• Contributing
  • Types of Contributions
    • Report Bugs
    • Fix Bugs
    • Implement Features
    • Write Documentation
    • Submit Feedback
  • Get Started!
  • Pull Request Guidelines
  • Tips

Indices

• Index

• Module Index

• Search Page