Abstract
This paper describes enhancement to genetic algorithms that allows them to escape from the local optima during the optimization. The proposed method relies on the search space transformation that helps in resumption of the search process. It allows to continue the optimization for the same population’s size without the random probing of the local optimum’s neighborhood. It also reduces a necessity for the genetic algorithm’s restart with a differently distributed initial population. In the result, it can converge to the global optimum after a lower number of iterations. The proposed method is applicable to optimization problems described by any number of real–valued variables. The paper describes this method and presents results from the experimental evaluation that highlights properties of the proposed method.
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Ziembiński, R. (2012). ReactGA – The Search Space Transformation for the Local Optimum Escaping. In: Corchado, E., Snášel, V., Abraham, A., Woźniak, M., Graña, M., Cho, SB. (eds) Hybrid Artificial Intelligent Systems. HAIS 2012. Lecture Notes in Computer Science(), vol 7208. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28942-2_43
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DOI: https://doi.org/10.1007/978-3-642-28942-2_43
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