Abstract
Genetic algorithm’s (GA’s) have become a powerful search tool pertaining to the identification of global optima within multimodal domains. Many different methodologies and techniques have been developed to aid in this search, and facilitate the efficient location of these optima. What has become known as Goldberg’s standard fitness sharing methodology is inefficient and does not explicitly identify or provide any information about the peaks (niches) of a fitness function. In this paper, a mechanism is formulated that will identify the peaks of a multimodal fitness function in a one-dimensional parameter space, using a hybrid form of clustering in the framework of a genetic algorithm. It is shown that the proposed Dynamic Niche Clustering scheme not only performs as well as standard nicheing, but works in O(nq) time, rather than O(n 2) time. In addition to this, it explicitly provides statistical information about the peaks themselves. The Dynamic Niche Clustering scheme is also shown to have favourable qualities in revealing multimodal function optima when there is little or no knowledge of the fitness function itself a priori.
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References
Beasley, D. & Bull, R.R. & Martin, R.R.: A Sequential Niche Technique for Multimodal Function Optimization, Evolutionary Computation 1(2), pp 101–125, MIT Press 1993.
Deb, K. & Goldberg, D.E.: An Investigation of Niche and Species Formation in Genetic Optimization, Proc 3rd Inter. Conf. Genetic Algorithms, pp 42–50, 1989.
Gan, J.: A Genetic Algorithm with Dynamic Niche Clustering for Multimodal Function Optimisation, (Internal Report No. 98-001) Cybernetics Dept, Reading University, 1998.
Goldberg, D.E. & Richardson, J.: Genetic Algorithms with Sharing for Multimodal Function Optimization, Proc. 2nd Inter. Conf. Genetic Algorithms, pp 41–49, 1987.
Goldberg, D.E.: Genetic Algorithms in Search Optimization & Machine Learning, Addison-Wesley 1989.
Goldberg, D.E. & Wang, L.: Adaptive Nicheing Via Co-evolutionary Sharing, Quaglianell et al (Eds) Genetic Algorithms in Engineering and Computer Science, pp21–38, John Wiley and Sons, Ltd. 1997.
Hanagandi, V. & Nikolaou, M.: A Hybrid Approach to Global Optimization using a Clustering Algorithm in a Genetic Search Framework, Computers and Chemical Engineering 1995.
Miler, B.L. & Shaw, M.J.: Genetic Algorithms with Dynamic Niche Sharing for Multimodal Function Optimization, IEEE International Conference on Evolutionary Computation, pp786–791, Piscataway, NJ: IEEE Press 1995.
Pictet, O.V. & Dacarogna, M.M. & Davé, R.D. & Chopard, B. & Schirru, R. & Tomassini, M.: Genetic Algorithms with Collective Sharing for Robust Optimization in Financial Applications, Olsen & Associates Working Paper (OVP. 1995-02-06) 1995.
Yin, X. & Germay, N.: A Fast Genetic Algorithm with Sharing Scheme Using Cluster Analysis Methods in Multimodal Function Optimization, Proc. Inter. Conf. Artificial Neural Nets and Genetic Algorithms, pp450–457, Innsbruck, Austria 1993.
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© 1999 Springer-Verlag Wien
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Gan, J., Warwick, K. (1999). A Genetic Algorithm with Dynamic Niche Clustering for Multimodal Function Optimisation. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6384-9_42
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DOI: https://doi.org/10.1007/978-3-7091-6384-9_42
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-83364-3
Online ISBN: 978-3-7091-6384-9
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