The population model of an evolutionary algorithm (EA) describes the structural properties of its population to which its members are subject. A population...

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...

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

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

genetic diversity of the chromosomes of a population of an evolutionary algorithm (EA), including genetic algorithms in particular. It is analogous to biological...

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

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

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

discovered every run, with no guarantee however. Evolutionary algorithms (EAs) due to their population based approach, provide a natural advantage over...

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

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

control tasks, the NEAT algorithm often arrives at effective networks more quickly than other contemporary neuro-evolutionary techniques and reinforcement...

nature-inspired algorithms" (PDF). Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation. pp...

evolutionary optimization uses evolutionary algorithms to search the space of hyperparameters for a given algorithm. Evolutionary hyperparameter optimization...

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

artificial intelligence that normalizes the data. Evolutionary programming is often paired with other algorithms e.g. artificial neural networks to improve the...

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

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

Cultural algorithms (CA) are a branch of evolutionary computation where there is a knowledge component that is called the belief space in addition to the...

Evolutionary game theory (EGT) is the application of game theory to evolving populations in biology. It defines a framework of contests, strategies, and...

on models which have been developed; the other purpose is to make predictions for future outcomes based on these models. A hypothetical algorithm specific...

programming (GEP) in computer programming is an evolutionary algorithm that creates computer programs or models. These computer programs are complex tree structures...

distribution algorithm (EDA) An evolutionary algorithm that substitutes traditional reproduction operators by model-guided operators. Such models are learned...

solutions and ending with the model that generates only the global optima. EDAs belong to the class of evolutionary algorithms. The main difference between...

genetic algorithms) and tree-like ones (as in genetic programming). Evolutionary art Human-based evolutionary computation Human-based genetic algorithm Human–computer...

breeding and in evolutionary algorithms from computer science, which selects a certain share of fittest individuals from a population for reproduction...

which work on a population of possible solutions in the context of evolutionary algorithms or in the context of swarm intelligence algorithms, are subdivided...

A genetic operator is an operator used in evolutionary algorithms (EA) to guide the algorithm towards a solution to a given problem. There are three main...