Abstract
This paper presents a genetic algorithm for solving fuzzy equations that evolves many sub-populations of solutions. The individuals are clustered into groups using a features based similarity measure. A distributed implementation of this segregative GA containing a communication mechanism that enforces the clustering structure of the sub-populations is described. The results of the experimental investigation of the ability to find multiple accurate roots as well as the effect of alternative distributed models and communication schemes are reported.
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Brudaru, O., Buzatu, O. (2008). Distributed Segregative Genetic Algorithm for Solving Fuzzy Equations. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Wasniewski, J. (eds) Parallel Processing and Applied Mathematics. PPAM 2007. Lecture Notes in Computer Science, vol 4967. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68111-3_50
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DOI: https://doi.org/10.1007/978-3-540-68111-3_50
Publisher Name: Springer, Berlin, Heidelberg
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