Combining incest prevention and multiplicity in evolutionary algorithms

Evolutionary Computation is an emergent field, which provides new heuristics to function optimization where traditional approaches make the problem computationally intractable. Exploration and exploitation of solution in the problem space are main issues affecting the performance of an evolutionary algorithm. Current enhancements attempt to balance exploitation and exploration to avoid premature convergence during the search process. Multiple parents multiple crossovers and incest prevention are three different techniques that when combined showed a substantial benefit: besides minimizing the risk of premature convergence, the final population is concentrated nearby the optimal solution. This behaviour is an important aid provided by the evolutionary process when applications require a set of alternative solutions to face system dynamics. This paper shows the design, implementation and partial performance results when incest prevention is combined with multiple crossovers on multiple parents for difficult multimodal optimization. Eje: Computación evolutiva Red de Universidades con Carreras en Informática (RedUNCI)