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1.
Population size reduction for the differential evolution algorithm
Janez Brest, Mirjam Sepesy Maučec, 2008, original scientific article

Abstract: This paper studies the efficiency of a recently defined population-based direct global optimization method called Differential Evolution with self-adaptive control parameters. The original version uses fixed population size but a method for gradually reducing population size is proposed in this paper. It improves the efficiency and robustness of the algorithm and can be applied to any variant of a Differential Evolution algorithm. The proposed modification is tested on commonly used benchmark problems for unconstrained optimization and compared with other optimization methods such as Evolutionary Algorithms and Evolution Strategies.
Keywords: differential evolution, control parameter, fitness function, global function optimization, self-adaptation, population size
Published: 01.06.2012; Views: 1201; Downloads: 64
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2.
Performance comparison of self-adaptive and adaptive differential evolution algorithms
Janez Brest, Borko Bošković, Sašo Greiner, Viljem Žumer, Mirjam Sepesy Maučec, 2007, original scientific article

Abstract: Differential evolution (DE) has been shown to be a simple, yet powerful, evolutionary algorithm for global optimization. for many real problems. Adaptation, especially self-adaptation, has been found to be highly beneficial for adjusting control parameters, especially when done without any user interaction. This paper presents differential evolution algorithms, whichuse different adaptive or self-adaptive mechanisms applied to the control parameters. Detailed performance comparisons of these algorithms on the benchmark functions are outlined.
Keywords: differential evolution, control parameter, fitness function, optimization, self-adaption
Published: 01.06.2012; Views: 1257; Downloads: 54
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