5 papers · 1 filter
Real-valued Evolutionary Multi-modal Multi-objective Optimization by Hill-Valley Clustering
S. C. Maree, T. Alderliesten, P. A. N. Bosman
In model-based evolutionary algorithms (EAs), the underlying search distribution is adapted to the problem at hand, for example based on dependencies between decision variables. Hi…
Uncrowded Hypervolume-based Multi-objective Optimization with Gene-pool Optimal Mixing
S. C. Maree, T. Alderliesten, P. A. N. Bosman
Domination-based multi-objective (MO) evolutionary algorithms (EAs) are today arguably the most frequently used type of MOEA. These methods however stagnate when the majority of th…
Benchmarking HillVallEA for the GECCO 2019 Competition on Multimodal Optimization
S. C. Maree, T. Alderliesten, P. A. N. Bosman
This report presents benchmarking results of the Hill-Valley Evolutionary Algorithm version 2019 (HillVallEA19) on the CEC2013 niching benchmark suite under the restrictions of the…
Real-Valued Evolutionary Multi-Modal Optimization driven by Hill-Valley Clustering
S. C. Maree, T. Alderliesten, D. Thierens +1
Model-based evolutionary algorithms (EAs) adapt an underlying search model to features of the problem at hand, such as the linkage between problem variables. The performance of EAs…
Benchmarking the Hill-Valley Evolutionary Algorithm for the GECCO 2018 Competition on Niching Methods Multimodal Optimization
S. C. Maree, T. Alderliesten, D. Thierens +1
This report presents benchmarking results of the latest version of the Hill-Valley Evolutionary Algorithm (HillVallEA) on the CEC2013 niching benchmark suite. The benchmarking foll…