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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 ]
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 .
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.
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 ...
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.
The competing conventions problem arises when there is more than one way of representing information in a phenotype. For example, if a genome contains neurons A, B and C and is represented by [A B C], if this genome is crossed with an identical genome (in terms of functionality) but ordered [C B A] crossover will yield children that are missing information ([A B A] or [C B C]), in fact 1/3 of ...
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.
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]