Evolutionary computation

Last updated Mar 20, 2023 Edit Source

In computer science, evolutionary computation is a family of algorithms for global optimization inspired by biological evolution, and the subfield of artificial intelligence and soft computing studying these algorithms. In technical terms, they are a family of population-based trial and error problem solvers with a metaheuristic or stochastic optimization character.

• https://en.wikipedia.org/wiki/Evolutionary_algorithm
• https://en.wikipedia.org/wiki/Evolutionary_computation
• Evolutionary Computation (FULL COURSE) Overview

• https://en.wikipedia.org/wiki/Genetic_algorithm
• Metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms. Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on bio-inspired operators such as mutation, crossover and selection
• What Is the Genetic Algorithm?
• Introduction to Genetic Algorithms — Including Example Code

• #CODE Evotorch - Advanced evolutionary computation library built directly on top of PyTorch
  • https://evotorch.ai/
  • Various evolutionary computation algorithms are available in EvoTorch.
    • Distribution-based search algorithms: PGPE - Policy Gradients with Parameter-based Exploration, XNES - Exponential Natural Evolution Strategies, CMA-ES - Covariance Matrix Adaptation Evolution Strategies, SNES - Separable Natural Evolution Strategies, CEM - Cross Entropy Method
    • Population-based search algorithms: GeneticAlgorithm - A genetic algorithm implementation. Also supports multiple objectives, in which case it behaves like NSGA-II, CoSyNE - Cooperative Synapse Neuroevolution, MAPElites - Multi-dimensional Archive of Phenotypic Elites
• #CODE DEAP - Distributed Evolutionary Algorithms in Python
  • http://deap.readthedocs.org/
• #CODE geneticalgorithm