Lexicographic codes: Error-correcting codes from game theory
IEEE Transactions on Information Theory
On the effectiveness of genetic search in combinatorial optimization
SAC '95 Proceedings of the 1995 ACM symposium on Applied computing
A simple heuristic based genetic algorithm for the maximum clique problem
SAC '98 Proceedings of the 1998 ACM symposium on Applied Computing
Adaptive, Restart, Randomized Greedy Heuristics for Maximum Clique
Journal of Heuristics
A Hybrid Genetic Algorithm for the Maximum Clique Problem
Proceedings of the 6th International Conference on Genetic Algorithms
How good are genetic algorithms at finding large cliques: an experimental
How good are genetic algorithms at finding large cliques: an experimental
Greedy closure evolutionary algorithms
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Dynamic local search for the maximum clique problem
Journal of Artificial Intelligence Research
Evolutionary approaches to the generation of optimal error correcting codes
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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The maximum possible number of codewords in a q-ary code of length n and minimum distance d is denoted Aq(n, d). It is a fundamental problem in coding theory to determine this value for given parameters q, n and d. Codes that attain the maximum are said to be optimal. Unfortunately, for many different values of these parameters, the maximum number of codewords is currently unknown: instead we have a known upper bound and a known lower bound for this value. In this paper, we investigate the use of different evolutionary algorithms for improving lower bounds for given parameters. We relate this problem to the well-known Maximum Clique Problem. We compare the performance of the evolutionary algorithms to Hill Climbing, Beam Search, Simulated Annealing, and greedy methods. We found that the GAs outperformed all other algorithms in general; furthermore, the difference in performance became more significant when considering harder test cases.