Summary
Hybrid metaheuristics have received considerable interest in recent years. A wide variety of hybrid approaches have been proposed in the literature. In this paper a new hybrid approach, named GA-EDA, is presented. This new hybrid algorithm is based on genetic and estimation of distribution algorithms. The original objective is to benefit from both approaches and attempt to achieve improved results in exploring the search space. In order to perform an evaluation of this new approach, a selection of synthetic optimization problems have been proposed, together with some real-world cases. Experimental results show the competitiveness of our new approach.
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Robles, V., Peña, J.M., Larrañaga, P., Pérez, M.S., Herves, V. (2006). GA-EDA: A New Hybrid Cooperative Search Evolutionary Algorithm. In: Lozano, J.A., Larrañaga, P., Inza, I., Bengoetxea, E. (eds) Towards a New Evolutionary Computation. Studies in Fuzziness and Soft Computing, vol 192. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32494-1_8
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