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Abstract: The Pareto Differential Evolution (PDE) algorithm was introduced last year and showed competitive results. The behavior of PDE, as in many other evolutionary multiobjective optimization (EMO) methods, varies according to the crossover and mutation rates. In this paper, we present a new version of PDE with self-adaptive crossover and mutation. We call the new version Self-adaptive Pareto Differential Evolution (SPDE). The emphasis of this paper is to analyze the dynamics and behavior of SPDE.... (Update)
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BibTeX entry: (Update)
H.A. Abbass. Self-adaptive pareto di#erential evolution. In The IEEE Congress on Evolutionary Computation, Honolulu, USA, pages 831--836. IEEE Press, 2002. http://citeseer.ist.psu.edu/abbass02selfadaptive.html More
@misc{ abbass02selfadaptive,
author = "H. Abbass",
title = "Self-adaptive pareto differential evolution",
text = "H.A. Abbass. Self-adaptive pareto differential evolution. In The IEEE Congress
on Evolutionary Computation, Honolulu, USA, pages 831--836. IEEE Press,
2002.",
year = "2002",
url = "citeseer.ist.psu.edu/abbass02selfadaptive.html" }
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3 erential evolution approach to vector optimisation problems (context) - Abbass, Sarkar et al. - 2001
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