A neuro-evolutionary approach to produce general hyper-heuristics for the dynamic variable ordering in hard binary constraint satisfaction problems

Authors:
José Carlos Ortiz-Bayliss;Hugo Terashima-Marin;Peter Ross;Jorge Iván Fuentes-Rosado;Manuel Valenzuela-Rendón
Affiliations:
Tecnológico de Monterrey, Monterrey, N .L., Mexico;Tecnológico de Monterrey, Monterrey, N .L., Mexico;Napier University, EdinbrughEH10 5DT, United Kingdom;Tecnológico de Monterrey, Monterrey, N.L., Mexico;Tecnológico de Monterrey, Monterrey, N.L., Mexico
Venue:
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Year:
2009

Citing 3
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Abstract

This paper introduces a neuro-evolutionary approach to produce hyper-heuristics for the dynamic variable ordering for hard binary constraint satisfaction problems. The model uses a GA to evolve a population of neural networks architectures and parameters. For every cycle in the GA process, the new networks are trained using backpropagation. When the process is over, the best trained individual in the last population of neural networks represents the general hyper-heuristic.