Mutation is a genetic operator used to maintain genetic diversity of the chromosomes of a population of an evolutionary algorithm (EA), including genetic...

Evolutionary programming is an evolutionary algorithm, where a share of new population is created by mutation of previous population without crossover...

Evolutionary algorithms (EA) reproduce essential elements of the biological evolution in a computer algorithm in order to solve “difficult” problems,...

genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA)....

Evolutionary computation from computer science is a family of algorithms for global optimization inspired by biological evolution, and the subfield of...

genotype in evolutionary algorithms (EA) is a set of parameters which define a proposed solution of the problem that the evolutionary algorithm is trying...

used in evolutionary algorithms (EA) to guide the algorithm towards a solution to a given problem. There are three main types of operators (mutation, crossover...

operations research, a memetic algorithm (MA) is an extension of an evolutionary algorithm (EA) that aims to accelerate the evolutionary search for the optimum...

adaptive and non-adaptive forces in driving evolutionary change is an area of current research. Mutation bias is usually conceived as a difference in...

In evolutionary computation, a human-based genetic algorithm (HBGA) is a genetic algorithm that allows humans to contribute solution suggestions to the...

Crossover in evolutionary algorithms and evolutionary computation, also called recombination, is a genetic operator used to combine the genetic information...

Selection is a genetic operator in an evolutionary algorithm (EA). An EA is a metaheuristic inspired by biological evolution and aims to solve challenging...

The Fly Algorithm is a computational method within the field of evolutionary algorithms, designed for direct exploration of 3D spaces in applications...

have also been successfully used and tested in evolutionary algorithms (EA) in general and genetic algorithms in particular, although the implementation of...

unwanted effect in evolutionary algorithms (EA), a metaheuristic that mimics the basic principles of biological evolution as a computer algorithm for solving...

convergence to a single solution. The field of Evolutionary algorithms encompasses genetic algorithms (GAs), evolution strategy (ES), differential evolution...

junctional diversity. Mutation is the ultimate source of all genetic variation, providing the raw material on which evolutionary forces such as natural...

science is a subclass of evolutionary algorithms, which serves as an optimization technique. It uses the major genetic operators mutation, recombination and...

Genetic programming (GP) is an evolutionary algorithm, an artificial intelligence technique mimicking natural evolution, which operates on a population...

via crossover and mutation Repeat steps 2-4 until satisfactory algorithm performance is reached or is no longer improving. Evolutionary optimization has...

designs are discarded and replaced with mutations and/or combinations of the better designs. This evolutionary algorithm continues until a prespecified amount...

neuro-evolution, is a form of artificial intelligence that uses evolutionary algorithms to generate artificial neural networks (ANN), parameters, and rules...

efficiently computing the likelihood of an evolutionary tree from nucleic acid sequence data. The algorithm is often used as a subroutine in a search for...

A cellular evolutionary algorithm (cEA) is a kind of evolutionary algorithm (EA) in which individuals cannot mate arbitrarily, but every one interacts...

computer science and operations research, the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems...

experimentally (outside of evolutionary computation), most current work has focused on the persistence of sexual reproduction over evolutionary time. The maintenance...

"What is an Evolutionary Algorithm?" (PDF). Archived (PDF) from the original on 9 August 2017. "What economists can learn from evolutionary theorists"...

important component of evolutionary algorithms (EA), such as genetic programming, evolution strategies or genetic algorithms. An EA is a metaheuristic...

of memetic algorithm is the use of a local search algorithm instead of or in addition to a basic mutation operator in evolutionary algorithms. A parallel...

column through a series of one or more point accepted mutations during a specified evolutionary interval, rather than these two amino acids being aligned...