Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Handbook of Parametric and Nonparametric Statistical Procedures
Handbook of Parametric and Nonparametric Statistical Procedures
A scalable multi-objective test problem toolkit
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Considerations in engineering parallel multiobjective evolutionary algorithms
IEEE Transactions on Evolutionary Computation
A multi-objective hyper-heuristic based on choice function
Expert Systems with Applications: An International Journal
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This work presents a set of improvements and a performance analysis for a previously designed multi-objective optimisation parallel model. The model is a hybrid algorithm that combines a parallel island-based scheme with a hyperheuristic approach in order to grant more computational resources to those schemes that show a more promising behaviour. The main aim is to raise the level of generality at which most current evolutionary algorithms operate. This way, a wider range of problems can be tackled since the strengths of one algorithm can compensate for the weaknesses of another. A contribution-based hyperheuristic previously presented in the literature is compared with a novel hypervolume-based hyperheuristic. The computational results obtained for some tests available in the literature demonstrate the superiority of the hypervolume-based hyperheuristic when compared to the contribution-based hyperheuristic and to other standard parallel models.