Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Journal of Global Optimization
Improving flexibility and efficiency by adding parallelism to genetic algorithms
Statistics and Computing
Spatially Structured Evolutionary Algorithms: Artificial Evolution in Space and Time (Natural Computing Series)
Parallel Metaheuristics: A New Class of Algorithms
Parallel Metaheuristics: A New Class of Algorithms
Parallel Combinatorial Optimization (Wiley Series on Parallel and Distributed Computing)
Parallel Combinatorial Optimization (Wiley Series on Parallel and Distributed Computing)
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Cellular Genetic Algorithms
Differential evolution algorithm with strategy adaptation for global numerical optimization
IEEE Transactions on Evolutionary Computation
Differential evolution using a neighborhood-based mutation operator
IEEE Transactions on Evolutionary Computation
JADE: adaptive differential evolution with optional external archive
IEEE Transactions on Evolutionary Computation
Theory and practice of cellular UMDA for discrete optimization
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Parallelism and evolutionary algorithms
IEEE Transactions on Evolutionary Computation
A scalable cellular implementation of parallel genetic programming
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
A hierarchical particle swarm optimizer and its adaptive variant
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Differential Evolution (DE) algorithms are efficient Evolutionary Algorithms (EAs) for the continuous optimization domain. There exist a large number of DE variants in the literature. In this paper, we analyze the effect of adding a cellular structure to the population of some of the most outstanding existing ones. The original algorithms will be compared versus their equivalent versions with cellular population both in terms of accuracy and convergence speed. As a result, we conclude that the cellular versions of the algorithms perform, in general, better than the equivalent state-of-the-art ones in the two considered issues.