Oct 25, 1995 · In this paper, we demonstrate a function that is GA-hard by analyzing the Walsh coefficients of this function's Walsh decomposition. Then, we ...
Experimental results show that DGAs (GAs using disruptive selection) perform very well on both GA-easy and GA-hard functions.
Bibliographic details on Why DGAs Work Well on GA-Hard Functions?
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The success of GAs could be explained from the fact they process all the building blocks contained into a single binary string at the same time, speeding-up the ...
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Abstract. Genetic Algorithms (GAs) have received a great deal of attention regarding their potential as optimization techniques for complex functions.
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According to the theory of deceptiveness in GAs, this method solves GA-easy and GA-hard problems efficiently, as shown effectively in the reported experiments.
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Apr 29, 2014 · Advantages of GAs compared to conventional methods: 1. Parallelism, easily modified and adaptable to different problems 2. Inherently parallel; easily ...
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Genetic Algorithms (GAs) have received a great deal of attention regarding their potential as optimization techniques for complex functions.
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This is known as implicit parallelism. In this sense, GAs perform decomposition of the problem to find the good building blocks that will be later assembled ...
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These explanations lead to a more fundamental question about GAs: what are the features of problems that determine the likelihood of successful GA performance?
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