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

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

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...

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

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...

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

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

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

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...

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

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

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

an algorithm is repeated and each run of the algorithm produces one Pareto optimal solution; Evolutionary algorithms where one run of the algorithm produces...

A. dos Santos-Paulino, J.-C. Nebel and F.Florez-Revuelta (2014) Evolutionary algorithm for dense pixel matching in presence of distortions, EvoStar Conference...

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

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...

parallel problem Emergent algorithm Evolutionary algorithm Fast Fourier transform Genetic algorithm Graph exploration algorithm Heuristic Hill climbing...

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

problems. They belong to the class of evolutionary algorithms and evolutionary computation. An evolutionary algorithm is broadly based on the principle of...

evolutionary algorithms (EA) to create specialized electronics without manual engineering. It brings together reconfigurable hardware, evolutionary computation...

Artificial Neural Networks and especially Deep Learning algorithms, but evolutionary algorithms such as particle swarm optimization can also be useful...

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

class of evolutionary algorithms. The main difference between EDAs and most conventional evolutionary algorithms is that evolutionary algorithms generate...

Scoring algorithm, also known as Fisher's scoring, is a form of Newton's method used in statistics to solve maximum likelihood equations numerically,...

or spinning wheel selection, is a selection technique used in evolutionary algorithms for selecting potentially useful solutions for recombination. In...

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

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

Thomas (1995). Evolutionary algorithms in theory and practice : evolution strategies, evolutionary programming, genetic algorithms. Oxford: Oxford University...