@inproceedings{11dc791a0dac420ebd6a994fd900be2f,
title = "An efficient heuristic-based evolutionary algorithm for solving constraint satisfaction problems",
abstract = "GENET and EGENET are artificial neural networks with remarkable success in solving hard constraint satisfaction problems (CSPs) such as car sequencing problems. (E)GENET uses the min-conflict heuristic in variable updating to find local minima, and then applies heuristic learning rule(s) to escape the local minima not representing solution(s). In this paper we describe a micro-genetic algorithm (MGA) which generalizes the (E)GENET approach for solving CSPs efficiently. Our proposed MGA integrates the min-conflict heuristic into mutation for reassigning allels (values) to genes (variables). In addition, we derive two methods, based on general principles from evolutionary algorithms, for escaping local minima: Population based learning, and look forward. Our preliminary experimental results showed that this evolutionary approach improved on EGENET in solving certain hard instances of CSPs.",
author = "V. Tam and P. Stuckey",
year = "1998",
month = jan,
day = "1",
doi = "10.1109/IJSIS.1998.685421",
language = "English",
isbn = "078034863X",
series = "Proceedings - IEEE International Joint Symposia on Intelligence and Systems",
publisher = "IEEE, Institute of Electrical and Electronics Engineers",
pages = "75--82",
booktitle = "Proceedings - IEEE International Joint Symposia on Intelligence and Systems",
address = "United States of America",
note = "1998 IEEE International Joint Symposia on Intelligence and Systems ; Conference date: 21-05-1998 Through 23-05-1998",
}