An adaptive evolutionary algorithm for the satisfiability problem
SAC '00 Proceedings of the 2000 ACM symposium on Applied computing - Volume 1
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Computers and Intractability: A Guide to the Theory of NP-Completeness
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Representations, Fitness Functions and Genetic Operators for the Satisfiability Problem
AE '97 Selected Papers from the Third European Conference on Artificial Evolution
A Superior Evolutionary Algorithm for 3-SAT
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
The Effects of Partial Restarts in Evolutionary Search
Selected Papers from the 5th European Conference on Artificial Evolution
Evolutionary Genetic Algorithms in a Constraint Satisfaction Problem: Puzzle Eternity II
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
ISICA '09 Proceedings of the 4th International Symposium on Advances in Computation and Intelligence
Autonomous operator management for evolutionary algorithms
Journal of Heuristics
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Adaptive fitness functions have led to very successful evolutionary algorithms for the satisfiability problem. Although comparisons are available for benchmarks, a deeper understanding of the effects of adaptation is desirable. Therefore, we compare three approaches based on adapting weights. The dynamics of these weights motivate the use of decay factors, which significantly improve the success rate for two adaptation schemes. The most successful technique can be further improved by accelerating the adaptation process concerning difficult clauses.