In genetic algorithms, the selection of individuals that will be part of future generations is a critical process of the algorithm. Various strategies exist to select these individuals: the general approach and the elitist approach. The general approach involves replacing the whole current population with the offspring generated so far. The elitist approach introduces a competitive element in which both parents and offspring compete for survival, and only fit individuals will be part of the next generation. While selecting fit individuals helps the algorithm to produce better results, the elitism has a major drawback: the premature convergence, which can limit the algorithm's overall performance. In this article, we compared a typical elitist genetic algorithm and an elitist algorithm improved with the parenting fitness parameter in resolving the vehicle routing problem with drones (VRPD). The parenting fitness parameter helps preserving diversity by retaining parents with high offspring potential despite of their personal fitness. The findings from the study demonstrates that integrating the parenting fitness parameter lead to better results in comparison with a typical elitist genetic algorithm, with relative improvement varying from 1.06% to 10.34% according to the dataset’s size.
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University of Hassan II Casablanca
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