ERA: an algorithm for reducing the epistasis of SAT problems

Authors:
Eduardo Rodriguez-Tello;Jose Torres-Jimenez
Affiliations:
ITESM Campus Cuernavaca, Computer Science Department, Temixco Morelos, Mexico;ITESM Campus Cuernavaca, Computer Science Department, Temixco Morelos, Mexico
Venue:
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Year:
2003

Citing 8
Cited 0

Foundations of genetic algorithms

Foundations of genetic algorithms
Planning as satisfiability

ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence

Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Computers and Intractability: A Guide to the Theory of NP-Completeness

Computers and Intractability: A Guide to the Theory of NP-Completeness
On Appropriate Adaptation Levels for the Learning of Gene Linkage

Genetic Programming and Evolvable Machines
A Mathematical Analysis of Tournament Selection

Proceedings of the 6th International Conference on Genetic Algorithms
Reducing the bandwidth of sparse symmetric matrices

ACM '69 Proceedings of the 1969 24th national conference

Quantified Score

Hi-index	0.00

Visualization

Abstract

A novel method, for solving satisfiability (SAT) instances is presented. It is based on two components: a) An Epistasis Reducer Algorithm (ERA) that produces a more suited representation (with lower epistasis) for a Genetic Algorithm (GA) by preprocessing the original SAT problem; and b) A Genetic Algorithm that solves the preprocesed instances. ERA is implemented by a simulated annealing algorithm (SA), which transforms the original SAT problem by rearranging the variables to satisfy the condition that the most related ones are in closer positions inside the chromosome. Results of experimentation demonstrated that the proposed combined approach outperforms GA in all the tests accomplished.