Cliques, Coloring, and Satisfiability: Second DIMACS Implementation Challenge, Workshop, October 11-13, 1993
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Improved Inapproximability Results for MaxClique, Chromatic Number and Approximate Graph Coloring
FOCS '01 Proceedings of the 42nd IEEE symposium on Foundations of Computer Science
Reinforcement learning agents with primary knowledge designed by analytic hierarchy process
Proceedings of the 2005 ACM symposium on Applied computing
A hybrid heuristic for the maximum clique problem
Journal of Heuristics
Adaptive Operator Selection for Iterated Local Search
SLS '09 Proceedings of the Second International Workshop on Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics
Dynamic local search for the maximum clique problem
Journal of Artificial Intelligence Research
An effective local search for the maximum clique problem
Information Processing Letters
Analyzing bandit-based adaptive operator selection mechanisms
Annals of Mathematics and Artificial Intelligence
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Most of standard evolutionary algorithms consist of a mutation, a crossover, a selection and often a local search. Each of these operators is specifically designed for a combinatorial optimization problem. These can be considered as tools for the optimization searches, and the interplay between them in the searches is not apparently controlled in many cases. In this paper, we present a flexible control method, called Strategic Controller (SC), for multiple mutation methods equipped in a memetic algorithm (MA) for the maximum clique problem (MCP). The SC is used to choose a suitable method from the candidate mutations. To perform an adaptive search, the SC evaluates each mutation method based on the contribution information which is served as novel "memes" for the mutations in the MA. To achieve the SC, we apply the idea of analytic hierarchy process. Although standard MAs have a population of multiple solutions as memes usually, a single solution is used in our MA. We evaluated the performance of MA with SC (MA-SC) on DIMACS benchmark graphs of the MCP. The results showed that MA-SC is capable of finding comprehensive solutions through comparisons with MAs in which each mutation is used. Moreover, we observed that it is highly effective particularly for hardest graphs in the benchmark set in comparisons with recent metaheuristics to the MCP.