Darren M. Chitty
ABSTRACT
Genetic Algorithms (GAs) have been commonly applied to difficult permutation problems such as the Traveling Salesman Problem (TSP). However, GAs have scalability issues when applied to very large-scale TSPs without resorting to local search. A GA relies on successively improving a population of solutions through crossover and mutation operations. However, these actions are performed blindly relying on natural selection to combine and improve upon good solutions. Alternatively, Ant Colony Optimisation simulates ants which learn the problem structure at a localised level via a pheromone matrix. This paper proposes that a GA could be accelerated by utilising the attributes of ants to construct solutions in a more intelligent manner, in effect hot-wiring GAs. This hypothesis is tested using ant-based mutation and an ant-based edge recombination crossover operator ER-ACO. Measured against art-based TSP instances of up to 200,000 cities both ant-based mutation and crossover improve GA evolution considerably.
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Index Terms
- Accelerating genetic algorithm evolution via ant-based mutation and crossover for application to large-scale TSPs
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