Comparer des méthodes

	Algorithme génétique	Évolution Différentielle
Domaine	Optimisation	Optimisation
Famille	Process / pipeline	Process / pipeline
Année d'origine	1975	1997
Auteur d'origine	John Henry Holland	Rainer Storn & Kenneth Price
Type	Population-based metaheuristic	Population-based stochastic metaheuristic
Source fondatrice	Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press.	Storn, R. & Price, K. (1997). Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. Journal of Global Optimization, 11(4), 341–359.
Alias	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon	DE algorithm, Diferansiyel Evrim (DE), DE optimization
Apparentées	5	5
Résumé	A genetic algorithm (GA) is a population-based metaheuristic optimization method introduced by John Henry Holland (1975) that mimics the principles of natural selection. It maintains a population of candidate solutions and iteratively improves them through selection, crossover, and mutation operators, making it especially powerful on discontinuous, non-convex, and multi-modal search spaces where classical gradient-based methods fail.	Differential Evolution (DE), introduced by Rainer Storn and Kenneth Price in 1997, is a population-based stochastic optimisation algorithm designed for continuous parameter spaces. It generates candidate solutions by combining vector differences between existing population members, making it a powerful and parameter-lean alternative to Genetic Algorithms and Particle Swarm Optimisation when the search landscape is non-convex, multimodal, or poorly suited to gradient-based methods.