Name
Title | ||
|---|---|---|
Multi-Objective optimization with an adaptive resonance theory-based estimation of distribution algorithm: a comparative study |
Abstract | ||
|---|---|---|
The introduction of learning to the search mechanisms of optimization algorithms has been nominated as one of the viable approaches when dealing with complex optimization problems, in particular with multi-objective ones. One of the forms of carrying out this hybridization process is by using multi-objective optimization estimation of distribution algorithms (MOEDAs). However, it has been pointed out that current MOEDAs have a intrinsic shortcoming in their model-building algorithms that hamper their performance. In this work we argue that error-based learning, the class of learning most commonly used in MOEDAs is responsible for current MOEDA underachievement. We present adaptive resonance theory (ART) as a suitable learning paradigm alternative and present a novel algorithm called multi-objective ART-based EDA (MARTEDA) that uses a Gaussian ART neural network for model-building and an hypervolume-based selector as described for the HypE algorithm. In order to assert the improvement obtained by combining two cutting-edge approaches to optimization an extensive set of experiments are carried out. These experiments also test the scalability of MARTEDA as the number of objective functions increases. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-25566-3_36 | LION |
Keywords | Field | DocType |
complex optimization problem,optimization algorithm,error-based learning,multi-objective art-based eda,multi-objective optimization estimation,comparative study,gaussian art neural network,multi-objective optimization,adaptive resonance,theory-based estimation,hype algorithm,current moedas,current moeda underachievement,distribution algorithm | Adaptive resonance theory,Mathematical optimization,Estimation of distribution algorithm,Computer science,Multi-objective optimization,Gaussian,Artificial intelligence,Optimization algorithm,Artificial neural network,Optimization problem,Machine learning,Scalability | Conference |
Citations | PageRank | References |
3 | 0.37 | 29 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luis Martí | 1 | 100 | 7.73 |
| Jesús García | 2 | 44 | 3.95 |
| Antonio Berlanga | 3 | 3 | 0.37 |
| José M. Molina | 4 | 604 | 67.82 |