2013 | OriginalPaper | Buchkapitel
High-Order Sequence Entropies for Measuring Population Diversity in the Traveling Salesman Problem
verfasst von : Yuichi Nagata, Isao Ono
Erschienen in: Evolutionary Computation in Combinatorial Optimization
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
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.