The history and status of the P versus NP question
STOC '92 Proceedings of the twenty-fourth annual ACM symposium on Theory of computing
A simplified NP-complete MAXSAT problem
Information Processing Letters
An adaptive evolutionary algorithm for the satisfiability problem
SAC '00 Proceedings of the 2000 ACM symposium on Applied computing - Volume 1
Artficial Immune Systems and Their Applications
Artficial Immune Systems and Their Applications
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
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
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
A Superior Evolutionary Algorithm for 3-SAT
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Constraint Processing
Self-Nonself Discrimination in a Computer
SP '94 Proceedings of the 1994 IEEE Symposium on Security and Privacy
Jigsaw Puzzles, Edge Matching, and Polyomino Packing: Connections and Complexity
Graphs and Combinatorics
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This work evaluates three evolutionary algorithms in a constraint satisfaction problem. Specifically, the problem is the Eternity II, a edge-matching puzzle with 256 unique square tiles that have to be placed on a square board of 16 × 16 cells. The aim is not to completely solve the problem but satisfy as many constraints as possible. The three evolutionary algorithms are: genetic algorithm, an own implementation of a technique based on immune system concepts and a multiobjective evolutionary algorithm developed from the genetic algorithm. In addition to comparing the results obtained by applying these evolutionary algorithms, they also will be compared with an exhaustive search algorithm (backtracking) and random search. For the evolutionary algorithms two different fitness functions will be used, the first one as the score of the puzzle and the second one as a combination of the multiobjective algorithm objectives. We also used two ways to create the initial population, one randomly and the other with some domain information.