Machine Learning
Bayesian optimization algorithm: from single level to hierarchy
Bayesian optimization algorithm: from single level to hierarchy
Drift and Scaling in Estimation of Distribution Algorithms
Evolutionary Computation
Addressing sampling errors and diversity loss in UMDA
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Analyzing probabilistic models in hierarchical BOA on traps and spin glasses
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Parameter cross-validation and early-stopping in univariate marginal distribution algorithm
Proceedings of the 9th annual conference on Genetic and evolutionary computation
UMDAs for dynamic optimization problems
Proceedings of the 10th 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
Analyzing probabilistic models in hierarchical BOA
IEEE Transactions on Evolutionary Computation - Special issue on evolutionary algorithms based on probabilistic models
Spurious dependencies and EDA scalability
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Hierarchical allelic pairwise independent functions
Proceedings of the 13th annual conference on Genetic and evolutionary computation
| Hi-index | 0.00 |
Estimation of Distribution Algorithms (EDAs) are a class of evolutionary algorithms that use machine learning techniques to solve optimization problems. Machine learning is used to learn probabilistic models of the selected population. This model is then used to generate next population via sampling. An important phenomenon in machine learning from data is called overfitting. This occurs when the model is overly adapted to the specifics of the training data so well that even noise is encoded. The purpose of this paper is to investigate whether overfitting happens in EDAs, and to discover its consequences. What is found is: overfitting does occur in EDAs; overfitting correlates to EDAs performance; reduction of overfitting using early stopping can improve EDAs performance.