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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.
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 .
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 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 ]
Artificial intelligence has traditionally used a top down approach, while alife generally works from the bottom up. [21] Artificial chemistry started as a method within the alife community to abstract the processes of chemical reactions. Evolutionary algorithms are a practical application of the weak alife principle applied to optimization ...
Michalski, R. S. (2000), "LEARNABLE EVOLUTION MODEL Evolutionary Processes Guided by Machine Learning", Machine Learning, 38: 9–40, doi: 10.1023/A:1007677805582 Michalski, R .S. (June 11–13, 1998), "Learnable Evolution: Combining Symbolic and Evolutionary Learning", Proceedings of the Fourth International Workshop on Multistrategy Learning ...
In some problem areas they are shown to be more efficient than traditional evolutionary algorithms. Bacteriologic algorithms (BA) inspired by evolutionary ecology and, more particularly, bacteriologic adaptation. Evolutionary ecology is the study of living organisms in the context of their environment, with the aim of discovering how they adapt.
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