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Intelligent Exploration for Genetic Algorithms
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Exploration vs. exploitation is a well known issue inEvolutionary Algorithms. Accordingly, an unbalanced search can leadto premature convergence. GASOM, a novel Genetic Algorithm,addresses this problem by intelligent exploration techniques. Theapproach uses Self-Organizing Maps to mine data from the evolutionprocess. The information obtained is successfully utilized toenhance the search strategy and confront genetic drift. This way,local optima are avoided and exploratory power is maintained. Theevaluation of GASOM on well known problems shows that iteffectively prevents premature convergence and seeks the globaloptimum. Particularly in deceptive and missleading functions itshowed outstanding performance. Additionally, representing thesearch history by the Self-Organizing Map provides a visuallypleasing insight into the state and course of evolution.
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