Abstract
This paper introduces a novel improved evolutionary algorithm, which combines genetic algorithms and hill climbing. Genetic Algorithms (GA) belong to a class of well established optimization meta-heuristics and their behavior are studied and analyzed in great detail. Various modifications were proposed by different researchers, for example modifications to the mutation operator. These modifications usually change the overall behavior of the algorithm. This paper presents a binary GA with a modified mutation operator, which is based on the well-known Hill Climbing Algorithm (HCA). The resulting algorithm, referred to as GAHC, also uses an elite tournament selection operator. This selection operator preserves the best individual from the GA population during the selection process while maintaining the positive characteristics of the standard tournament selection. This paper discusses the GAHC algorithm and compares its performance with standard GA.
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© 2008 Springer-Verlag Berlin Heidelberg
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Matoušek, R. (2008). GAHC: Improved Genetic Algorithm. In: Krasnogor, N., Nicosia, G., Pavone, M., Pelta, D. (eds) Nature Inspired Cooperative Strategies for Optimization (NICSO 2007). Studies in Computational Intelligence, vol 129. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78987-1_46
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DOI: https://doi.org/10.1007/978-3-540-78987-1_46
Publisher Name: Springer, Berlin, Heidelberg
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