Abstract
This paper presents the hybrid approach of Cuckoo Search (CS) and Genetic Algorithm (GA) algorithms for solving optimization problems. In standard CS, each cuckoo lays one egg at a time, but in the proposed hybrid algorithm, in order to lay more eggs we used the genetic algorithms’ strategy (Crossover) for their reproduction. According to the cuckoos breeding style, each nest will have one cuckoo at a time. Since there is limitation in number of nests we will have a selection for all cuckoos. Furthermore, we added mutation in order to reduce the chance of eggs to be discovered, because cuckoo birds are specialized in mimicry in color and pattern of the host birds. This theory gets us closer to their real living style. Experimental results are examined with some standard benchmark functions and the results are reported.
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Ghodrati, A., Lotfi, S. (2012). A Hybrid CS/GA Algorithm for Global Optimization. In: Deep, K., Nagar, A., Pant, M., Bansal, J. (eds) Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) December 20-22, 2011. Advances in Intelligent and Soft Computing, vol 130. Springer, India. https://doi.org/10.1007/978-81-322-0487-9_38
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DOI: https://doi.org/10.1007/978-81-322-0487-9_38
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