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An evolutionary algorithm (EA) in computational intelligence is a subset of evolutionary computation, [1] a generic population-based metaheuristic optimization algorithm. An EA uses mechanisms inspired by biological evolution , such as reproduction , mutation , recombination and selection .
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.
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 .
MarI/O - Machine Learning for Video Games, a YouTube video demonstrating an implementation of NEAT learning to play Super Mario World "GekkoQuant.com" - A visual tutorial series on NEAT, including solving the classic pole balancing problem using NEAT in R "Artificial intelligence learns Mario level in just 34 attempts NEAT explained via MarI/O ...
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 ]
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 algorithms is a sub-field of evolutionary computing. Evolution strategies (ES, see Rechenberg, 1994) evolve individuals by means of mutation and intermediate or discrete recombination. ES algorithms are designed particularly to solve problems in the real-value domain. [58]
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.