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 |
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