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
Optimal implementations of UPGMA and other common clustering algorithms
Information Processing Letters
Why one must use reweighting in estimation of distribution algorithms
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
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
Spurious dependencies and EDA scalability
Proceedings of the 12th annual conference on Genetic and evolutionary computation
The linkage tree genetic algorithm
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Optimal mixing evolutionary algorithms
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Pairwise and problem-specific distance metrics in the linkage tree genetic algorithm
Proceedings of the 13th annual conference on Genetic and evolutionary computation
On measures to build linkage trees in LTGA
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
More concise and robust linkage learning by filtering and combining linkage hierarchies
Proceedings of the 15th annual conference on Genetic and evolutionary computation
On the usefulness of linkage processing for solving MAX-SAT
Proceedings of the 15th 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
| Hi-index | 0.00 |
Recently, the Linkage Tree Genetic Algorithm (LTGA) was introduced as one of the latest developments in a line of EA research that studies building models to capture and exploit linkage information between problem variables. LTGA was reported to exhibit excellent performance on several linkage benchmark problems, mainly attributed to use of the LT linkage model. In this paper we consider a technique called Forced Improvements (FI), that allows LTGA to converge to a single solution without requiring an explicit, diversity-reducing, selection step. We further consider a different linkage model, called Linkage Neighbors (LN), that is more flexible, yet can be learned equally efficiently from data. Even with the simplest learning approach for configuring the LN, better results are obtained on the linkage benchmark problems than when the LT model is used. However, on weighted MAXCUT (a combinatorial optimization problem), very poor results are obtained and a more involved multiscale LN variant is required to obtain a performance near that of LTGA. Our results underline the advantage of processing linkage in a single model on multiple scales as well as the importance of also considering problems other problems than common linkage benchmark problems when judging the merits of linkage learning techniques.