What Makes a Problem Hard for a Genetic Algorithm? Some Anomalous Results and Their Explanation
Machine Learning - Special issue on genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
A Survey of Optimization by Building and Using Probabilistic Models
Computational Optimization and Applications
Modeling Building-Block Interdependency
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
From Recombination of Genes to the Estimation of Distributions I. Binary Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
The Estimation of Distributions and the Minimum Relative Entropy Principle
Evolutionary Computation
Does overfitting affect performance in estimation of distribution algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Fluctuating crosstalk, deterministic noise, and GA scalability
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Conquering hierarchical difficulty by explicit chunking: substructural chromosome compression
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Towards billion-bit optimization via a parallel estimation of distribution algorithm
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Why is parity hard for estimation of distribution algorithms?
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Difficulty of linkage learning in estimation of distribution algorithms
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Spurious dependencies and EDA scalability
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Substructural neighborhoods for local search in the bayesian optimization algorithm
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
IEEE Transactions on Evolutionary Computation
Higher-order linkage learning in the ECGA
Proceedings of the 14th annual conference on Genetic and evolutionary computation
| Hi-index | 0.00 |
Current multivariate EDAs rely on computationally efficient pairwise linkage detection mechanisms to identify higher order linkage blocks. Historical attempts to exemplify the potential disadvantage of this computational shortcut were scarcely successful. In this paper we introduce a new class of test functions to exemplify the inevitable weakness of the simplified linkage learning techniques. Specifically, we show that presently employed EDAs are not able to efficiently mix and decide between building-blocks with pairwise allelic independent components. These problems can be solved by EDAs only at the expense of exploring a vastly larger search space of multivariable linkages.