Noise strategies for improving local search
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
A Computing Procedure for Quantification Theory
Journal of the ACM (JACM)
Future Generation Computer Systems
A machine program for theorem-proving
Communications of the ACM
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Evolutionary algorithms for the satisfiability problem
Evolutionary Computation
Genetic Algorithm Behavior in the MAXSAT Domain
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
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
The complexity of theorem-proving procedures
STOC '71 Proceedings of the third annual ACM symposium on Theory of computing
Where the really hard problems are
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Ants can solve constraint satisfaction problems
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Ant Colony Optimization (ACO) is a metaheuristic inspired by the foraging behavior of ant colonies that has been successful in the resolution of hard combinatorial optimization problems. This work proposes MaxMin-SAT, an ACO alternative for the satisfiability problem (SAT). MaxMin-SAT is the first ACO algorithm for SAT that implements an Adaptive Fitness Function, which is a technique used for Genetic Algorithms to escape local optima. To show effectiveness of this technique, three different adaptive fitness functions are compared: Stepwise Adaptation of Weights, Refining Functions, and a mix of the previous two. To experimentally test MaxMin-SAT, a comparison with Walksat (a successful local search algorithm) is presented. Even though MaxMin-SAT cannot beat Walksat when dealing with phase transition instances, experimental results show that it can be competitive with the local search heuristic for overconstrained instances.