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A Synergistic Selection Strategy in the Genetic Algorithms

Authors: Ting Kuo;

A Synergistic Selection Strategy in the Genetic Algorithms

Abstract

According to the Neo-Darwinist, natural selection can be classified into three categories: directional selection, disruptive selection, and stabilizing selection. Traditional genetic algorithms can be viewed as a process of evolution based on directional selection that gives more chances of reproduction to superior individuals. However, this strategy sometimes is myopic and is apt to trap the search into a local optimal. Should we restrict genetic algorithms to direction selection? No! First, we show that stabilizing selection and disruptive selection are complementary and that hybridize them may supersede directional selection. Then, we adopt an island model of parallel genetic algorithms on which two types of selection strategies are applied to two subpopulations that both evolve independently and migration is allowed between them periodically. Experimental results show that the cooperation of disruptive selection and stabilizing selection is an effective and robust way in the genetic algorithms.

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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