Multimeme Algorithms for Protein Structure Prediction
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Ant Colony Optimization
Strongly typed genetic programming
Evolutionary Computation
Evolving an edge selection formula for ant colony optimization
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Evolutionary design of Evolutionary Algorithms
Genetic Programming and Evolvable Machines
A Field Guide to Genetic Programming
A Field Guide to Genetic Programming
Evolving strategies for updating pheromone trails: a case study with the TSP
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
Designing pheromone update strategies with strongly typed genetic programming
EuroGP'11 Proceedings of the 14th European conference on Genetic programming
Extending particle swarm optimisation via genetic programming
EuroGP'05 Proceedings of the 8th European conference on Genetic Programming
Evolving the structure of the particle swarm optimization algorithms
EvoCOP'06 Proceedings of the 6th European conference on Evolutionary Computation in Combinatorial Optimization
Automatic design of ant algorithms with grammatical evolution
EuroGP'12 Proceedings of the 15th European conference on Genetic Programming
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We propose a computational framework for the self-generation of components used by an Ant Colony Optimization algorithm. The approach relies on Strongly Typed Genetic Programming to automatically seek for effective update pheromone strategies. Best evolved strategies are then inserted in an Ant Colony Algorithm used to find good quality solutions for the Quadratic Assignment Problem. Results reveal that evolved update rules are competitive with human designed variants and can be effectively reused on different instances of the same problem. Moreover, we investigate the possibility of evolving general strategies that can be used across different optimization problems.