Abstract
We propose two entropy-based diversity measures for evaluating population diversity in a genetic algorithm (GA) applied to the traveling salesman problem (TSP). In contrast to a commonly used entropy-based diversity measure, the proposed ones take into account high-order dependencies between the elements of individuals in the population. More precisely, the proposed ones capture dependencies in the sequences of up to m + 1 vertices included in the population (tours), whereas the commonly used one is the special case of the proposed ones with m = 1. We demonstrate that the proposed entropy-based diversity measures with appropriate values of m evaluate population diversity more appropriately than does the commonly used one.
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Nagata, Y., Ono, I. (2013). High-Order Sequence Entropies for Measuring Population Diversity in the Traveling Salesman Problem. In: Middendorf, M., Blum, C. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2013. Lecture Notes in Computer Science, vol 7832. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37198-1_16
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DOI: https://doi.org/10.1007/978-3-642-37198-1_16
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