The Scientific World Journal / 2014 / Article / Tab 1

Research Article

Crossover versus Mutation: A Comparative Analysis of the Evolutionary Strategy of Genetic Algorithms Applied to Combinatorial Optimization Problems

Table 1

Summary of the characteristics of all the techniques developed.

Alg. Pop. Crossover function (TSP, BPP, NQP) Mutation function (TSP, BPP, NQP) Crossover function (CVRP) Mutation function (CVRP)

50 90% 10% OX 2-opt SRX VIF
50 90% 10% OBX 2-opt RRX VIF
50 0% 100% No cross. 2-opt No cross. VIF

75 75% 25% HX IF LRX SF
75 75% 25% MOX IF SRX SF
75 0% 100% No cross. IF No cross. SF

100 50% 50% OBX 2-opt RRX VIF
100 50% 50% OX 2-opt LRX VIF
100 0% 100% No cross. 2-opt No cross. VIF