Cross-entropy and rare events for maximal cut and partition problems
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue: Rare event simulation
Proceedings of the 5th International Conference on Genetic Algorithms
Towards a New Evolutionary Computation: Advances on Estimation of Distribution Algorithms (Studies in Fuzziness and Soft Computing)
Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications (Studies in Computational Intelligence)
Optimal implementations of UPGMA and other common clustering algorithms
Information Processing Letters
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Solving Max-Cut to optimality by intersecting semidefinite and polyhedral relaxations
Mathematical Programming: Series A and B
Linkage neighbors, optimal mixing and forced improvements in genetic algorithms
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Hierarchical problem solving with the linkage tree genetic algorithm
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Genepool Optimal Mixing Evolutionary Algorithms (GOMEAs) were recently proposed as a new way of designing linkage-friendly, efficiently-scalable evolutionary algorithms (EAs). GOMEAs combine the building of linkage models with an intensive, greedy mixing procedure. Recent results indicate that the use of hierarchical linkage models in GOMEAs lead to the most robust and efficient performance. Two of such GOMEA instances are the Linkage Tree Genetic Algorithm (LTGA) and the Multi-scale Linkage Neighbors Genetic Algorithm (MLNGA). The linkage models in these GOMEAs have their individual merits and drawbacks. In this paper, we propose enhancement techniques targeted at filtering out superfluous linkage sets from hierarchical linkage models and we consider a way to construct a linkage model that combines the strengths of different linkage models. We then propose a new GOMEA instance, called the Linkage Trees and Neighbors Genetic Algorithm (LTNGA), that combines the models of LTGA and MLNGA. LTNGA performs comparable or better than the best of either LTGA or MLNGA on various problems, including typical linkage benchmark problems and instances of the well-known combinatorial problem MAXCUT, especially when the proposed filtering techniques are used.