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Evolutionary algorithms (EA) reproduce essential elements of the biological evolution in a computer algorithm in order to solve “difficult” problems, at least approximately, for which no exact or satisfactory solution methods are known.
Distributed Evolutionary Algorithms in Python (DEAP) is an evolutionary computation framework for rapid prototyping and testing of ideas. [2] [3] [4] It incorporates the data structures and tools required to implement most common evolutionary computation techniques such as genetic algorithm, genetic programming, evolution strategies, particle swarm optimization, differential evolution, traffic ...
Genetic programming (GP) is an evolutionary algorithm, an artificial intelligence technique mimicking natural evolution, which operates on a population of programs.It applies the genetic operators selection according to a predefined fitness measure, mutation and crossover.
Evolutionary computation from computer science is a family of algorithms for global optimization inspired by biological evolution, and the subfield of artificial intelligence and soft computing studying these algorithms.
Evolutionary programming is an evolutionary algorithm, where a share of new population is created by mutation of previous population without crossover. [ 1 ] [ 2 ] Evolutionary programming differs from evolution strategy ES( μ + λ {\displaystyle \mu +\lambda } ) in one detail. [ 1 ]
Pages in category "Evolutionary algorithms and artificial neuronal networks" This category contains only the following page. This list may not reflect recent changes .
Neuroevolution, or neuro-evolution, is a form of artificial intelligence that uses evolutionary algorithms to generate artificial neural networks (ANN), parameters, and rules. [1] It is most commonly applied in artificial life , general game playing [ 2 ] and evolutionary robotics .
An evolutionary algorithm (EA) is a heuristic optimization algorithm using techniques inspired by mechanisms from organic evolution such as mutation, recombination, and natural selection to find an optimal configuration for a specific system within specific constraints.