Automatic design of ant algorithms with grammatical evolution

Authors:
Jorge Tavares;Francisco B. Pereira
Affiliations:
CISUC, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal;CISUC, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal and ISEC, Coimbra, Portugal
Venue:
EuroGP'12 Proceedings of the 15th European conference on Genetic Programming
Year:
2012

Citing 9
Cited 3

Ant Colony Optimization

Ant Colony Optimization
Evolving an edge selection formula for ant colony optimization

Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Automatic configuration of multi-objective ACO algorithms

ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
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
Towards the development of self-ant systems

Proceedings of the 13th annual conference on Genetic and evolutionary computation
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
Parameter control in evolutionary algorithms

IEEE Transactions on Evolutionary Computation

Evolving evolutionary algorithms

Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
The importance of the learning conditions in hyper-heuristics

Proceedings of the 15th annual conference on Genetic and evolutionary computation
Vector-valued function estimation by grammatical evolution for autonomous robot control

Information Sciences: an International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

We propose a Grammatical Evolution approach to the automatic design of Ant Colony Optimization algorithms. The grammar adopted by this framework has the ability to guide the learning of novel architectures, by rearranging components regularly found on human designed variants. Results obtained with several TSP instances show that the evolved algorithmic strategies are effective, exhibit a good generalization capability and are competitive with human designed variants.