Tabu search for nonlinear and parametric optimization (with links to genetic algorithms)
Discrete Applied Mathematics - Special volume: viewpoints on optimization
Uniform Crossover in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
A Template for Scatter Search and Path Relinking
AE '97 Selected Papers from the Third European Conference on Artificial Evolution
New heuristics for the maximum diversity problem
Journal of Heuristics
Variable neighborhood search for the heaviest k-subgraph
Computers and Operations Research
Handbook of Memetic Algorithms
Handbook of Memetic Algorithms
GRASP with path-relinking for the maximum diversity problem
WEA'05 Proceedings of the 4th international conference on Experimental and Efficient Algorithms
Learnable tabu search guided by estimation of distribution for maximum diversity problems
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Computational aspects of the maximum diversity problem
Operations Research Letters
Heuristics and metaheuristics for the maximum diversity problem
Journal of Heuristics
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This paper presents a highly effective memetic algorithm for the maximum diversity problem based on tabu search. The tabu search component uses a successive filter candidate list strategy and the solution combination component employs a combination operator based on identifying strongly determined and consistent variables. Computational experiments on three sets of 40 popular benchmark instances indicate that our tabu search/memetic algorithm (TS/MA) can easily obtain the best known results for all the tested instances (where no previous algorithm has achieved) as well as improved results for six instances. Analysis of comparisons with state-of-the-art algorithms demonstrates statistically that our TS/MA competes very favorably with the best performing algorithms. Key elements and properties of TS/MA are also analyzed to disclose the benefits of integrating tabu search (using a successive filter candidate list strategy) and solution combination (based on critical variables).