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Evolutionary bayesian classifier-based optimization in continuous domains.
| Content Provider | CiteSeerX |
|---|---|
| Author | Miquélez, Teresa Bengoetxea, Endika Larrañaga, Pedro |
| Abstract | Abstract. In this work, we present a generalisation to continuous domains of an optimization method based on evolutionary computation that applies Bayesian classifiers in the learning process. The main difference between other estimation of distribution algorithms (EDAs) and this new method –known as Evolutionary Bayesian Classifier-based Optimization Algorithms (EBCOAs) – is the way the fitness function is taken into account, as a new variable, to generate the probabilistic graphical model that will be applied for sampling the next population. We also present experimental results to compare performance of this new method with other methods of the evolutionary computation field like evolution strategies, and EDAs. Results obtained show that this new approach can at least obtain similar performance as these other paradigms 1. 1 |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Evolutionary Computation Field Main Difference New Approach Fitness Function Evolutionary Computation Next Population Probabilistic Graphical Model Learning Process Present Experimental Result Continuous Domain Optimization Method Evolution Strategy Similar Performance New Variable Distribution Algorithm Evolutionary Bayesian Classifier-based Optimization Algorithm Evolutionary Bayesian Classifier-based Optimization Bayesian Classifier New Method |
| Content Type | Text |
