An empirical study of genetic operators in genetic algorithms
EUROMICRO 93 Nineteenth EUROMICRO symposium on microprocessing and microprogramming on Open system design : hardware, software and applications: hardware, software and applications
Adaptation to a Changing Environment by Means of the Feedback Thermodynamical Genetic Algorithm
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
A study of multi-parent crossover operators in a memetic algorithm
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Efficient population diversity handling genetic algorithm for qos-aware web services selection
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part IV
Fast EAX algorithm considering population diversity for traveling salesman problems
EvoCOP'06 Proceedings of the 6th European conference on Evolutionary Computation in Combinatorial Optimization
An evolutionary algorithm for large traveling salesman problems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Powerful Genetic Algorithm Using Edge Assembly Crossover for the Traveling Salesman Problem
INFORMS Journal on Computing
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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.