Abstract
This paper introduces a new approach, called recurring multistage evolutionary algorithm (RMEA), to balance the explorative and exploitative features of the conventional evolutionary algorithm. Unlike most previous work, the basis of RMEA is repeated and alternated executions of two different stages i.e. exploration and exploitation during evolution. RMEA uses dissimilar information across the population and similar information within population neighbourhood in mutation operation for achieving global exploration and local exploitation, respectively. It is applied on two unimodal, two multimodal, one rotated multimodal and one composition functions. The experimental results indicated the effectiveness of using different object-oriented stages and their repeated alternation during evolution. The comparison of RMEA with other algorithms showed its superiority on complex problems.
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Islam, M.M., Alam, M.S., Murase, K. (2007). A New Recurring Multistage Evolutionary Algorithm for Solving Problems Efficiently. In: Yin, H., Tino, P., Corchado, E., Byrne, W., Yao, X. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2007. IDEAL 2007. Lecture Notes in Computer Science, vol 4881. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77226-2_11
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DOI: https://doi.org/10.1007/978-3-540-77226-2_11
Publisher Name: Springer, Berlin, Heidelberg
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