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Relationship between Genetic Algorithms and Ant Colony Optimization Algorithms

Genetic Algorithms (GAs) were introduced by Holland as a computational analogy of adaptive systems. GAs are search procedures based on the mechanics of natural selection and natural genetics. Ant Colony Optimization (ACO) is a metaheuristic inspired by the foraging behavior of ant colonies. ACO was introduced by Dorigo and has evolved significantly in the last few years. Both algorithms have shown their effectiveness in the resolution of hard combinatorial optimization problems. This paper shows the relationship between these two evolutionary algorithms. This relationship extends the reasons of ACO’s success in TSP to GAs. Finally, the significance of the crossover and the genetic diversity in globally convex structures is explained.