Evolution of neural networks topologies and learning parameters to produce hyper-heuristics for constraint satisfaction problems

Authors:
José Carlos Ortiz-Bayliss;Hugo Terashima-Marín;Peter Ross;Santiago Enrique Conant-Pablos
Affiliations:
Tecnológico de Monterrey, Monterrey, Mexico;Tecnológico de Monterrey, Monterrey, Mexico;University of Napier, Edinburgh, Scotland Uk;Tecnológico de Monterrey, Monterrey, Mexico
Venue:
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Year:
2011

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Abstract

This paper describes a model which constructs hyper-heuristics for variable ordering within Constraint Satisfaction Problems (CSPs) by running a genetic algorithm that evolves the topology of neural networks and some learning parameters.