Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
Knapsack problems: algorithms and computer implementations
Knapsack problems: algorithms and computer implementations
Elements of information theory
Elements of information theory
The sciences of the artificial (3rd ed.)
The sciences of the artificial (3rd ed.)
Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms
Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A Genetic Algorithm for the Multidimensional Knapsack Problem
Journal of Heuristics
On the Feasibility Problem of Penalty-Based Evolutionary Algorithms for Knapsack Problems
Proceedings of the EvoWorkshops on Applications of Evolutionary Computing
Optimal implementations of UPGMA and other common clustering algorithms
Information Processing Letters
There is no EPTAS for two-dimensional knapsack
Information Processing Letters
NK landscapes, problem difficulty, and hybrid evolutionary algorithms
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
Multi-objective phylogenetic algorithm: solving multi-objective decomposable deceptive problems
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
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
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Multidimensional Knapsack Problem: A Fitness Landscape Analysis
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The minimum description length principle in coding and modeling
IEEE Transactions on Information Theory
Linkage tree genetic algorithms: variants and analysis
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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Linkage Learning (LL) is an important issue concerning the development of more effective genetic algorithms (GA). It is from the identification of strongly dependent variables that crossover can be effective and an efficient search can be implemented. In the last decade many algorithms have confirmed the beneficial influence of LL when solving nearly decomposable problems. As it is a well-known fact from the no free-lunch theorem, LL can not be the best tool for all optimization problems, therefore, methods to identify those problems which could be efficiently solved by LL have become necessary. This paper investigates that nearly-decomposable problems present characteristic linkage-trees, therefore, those trees can be used as reference to infer whether or not some black-box optimization problem is a good candidate to be solved by LL. In this context, we consider the linkage-tree model from the Linkage-Tree GA (LTGA) and use the silhouette measure to expose some problems' characteristics. The silhouette fingerprints (SF) are defined for overlapping deceptive trap functions and compared with the SFs obtained for Multidimensional Knapsack Problems (MKP). The comparison allowed us to conclude that MKPs do not present evident linkages. This result was confirmed by experiments comparing the performance of the LTGA and the Randomized LTGA, in which both algorithms had very similar results.