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	Ewolucja Różnicowa	Algorytm świetlików	Algorytm genetyczny
Dziedzina	Optymalizacja	Optymalizacja	Optymalizacja
Rodzina	Process / pipeline	Process / pipeline	Process / pipeline
Rok powstania	1997	2008	1975
Twórca	Rainer Storn & Kenneth Price	Xin-She Yang	John Henry Holland
Typ	Population-based stochastic metaheuristic	Swarm intelligence metaheuristic	Population-based metaheuristic
Źródło pierwotne	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.	Yang, X.S. (2010). Firefly Algorithm, Stochastic Test Functions and Design Optimisation. International Journal of Bio-Inspired Computation, 2(2), 78-84.	Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press.
Inne nazwy	DE algorithm, Diferansiyel Evrim (DE), DE optimization	FA, Firefly Optimization, Ateşböceği Algoritması (Firefly Algorithm)	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon
Pokrewne	5	5	5
Podsumowanie	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.	The Firefly Algorithm (FA), introduced by Xin-She Yang in 2008 and formally published in 2010, is a nature-inspired swarm metaheuristic that models the bioluminescent attraction behaviour of fireflies. Each candidate solution is a firefly whose brightness represents its objective-function value; dimmer fireflies move toward brighter ones with an attraction force that decays with distance, driving the swarm toward optima without gradient information.	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.