Summary
In this paper, we propose a replacement strategy for steady-state genetic algorithms that takes into account two features of the element to be included into the population: a measure of the contribution of diversity to the population and the fitness function. In particular, the proposal attempts to replace an element in the population with worse values for these two features. In this way, the diversity of the population is increased and the quality of its solutions is improved, simultaneously, maintaining high levels of useful diversity. Experimental results show that the use of the proposed replacement strategy allows significant performance to be achieved for problems with different difficulties, which regards to other replacement strategies presented in the literature.
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Lozano, M., Herrera, F., Cano, J.R. (2005). Replacement Strategies to Maintain Useful Diversity in Steady-State Genetic Algorithms. In: Hoffmann, F., Köppen, M., Klawonn, F., Roy, R. (eds) Soft Computing: Methodologies and Applications. Advances in Soft Computing, vol 32. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32400-3_7
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DOI: https://doi.org/10.1007/3-540-32400-3_7
Publisher Name: Springer, Berlin, Heidelberg
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