Search Results for author: P. A. N. Bosman

Found 10 papers, 5 papers with code

Function Class Learning with Genetic Programming: Towards Explainable Meta Learning for Tumor Growth Functionals

no code implementations19 Feb 2024 E. M. C. Sijben, J. C. Jansen, P. A. N. Bosman, T. Alderliesten

The aim of this work is to learn the general underlying growth pattern of paragangliomas from multiple tumor growth data sets, in which each data set contains a tumor's volume over time.

Meta-Learning

Real-valued Evolutionary Multi-modal Multi-objective Optimization by Hill-Valley Clustering

1 code implementation28 Oct 2020 S. C. Maree, T. Alderliesten, P. A. N. Bosman

This can be used to adapt the search distribution of an EA to the number of modes, exploring each mode separately.

Clustering Evolutionary Algorithms

Local Search is a Remarkably Strong Baseline for Neural Architecture Search

2 code implementations20 Apr 2020 T. Den Ottelander, A. Dushatskiy, M. Virgolin, P. A. N. Bosman

The proposed LS algorithm is compared with RS and two evolutionary algorithms (EAs), as these are often heralded as being ideal for multi-objective optimization.

Evolutionary Algorithms Image Classification +1

Uncrowded Hypervolume-based Multi-objective Optimization with Gene-pool Optimal Mixing

2 code implementations10 Apr 2020 S. C. Maree, T. Alderliesten, P. A. N. Bosman

The resulting algorithm, UHV-GOMEA, is compared to Sofomore equipped with GOMEA, and the domination-based MO-GOMEA.

Evolutionary Algorithms

Benchmarking HillVallEA for the GECCO 2019 Competition on Multimodal Optimization

1 code implementation25 Jul 2019 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 GECCO 2019 niching competition on multimodal optimization.

Benchmarking

Real-Valued Evolutionary Multi-Modal Optimization driven by Hill-Valley Clustering

no code implementations16 Oct 2018 S. C. Maree, T. Alderliesten, D. Thierens, P. A. N. Bosman

The performance of EAs often deteriorates as multiple modes in the fitness landscape are modelled with a unimodal search model.

Clustering Evolutionary Algorithms

Benchmarking the Hill-Valley Evolutionary Algorithm for the GECCO 2018 Competition on Niching Methods Multimodal Optimization

1 code implementation30 Jun 2018 S. C. Maree, T. Alderliesten, D. Thierens, P. A. N. Bosman

This report presents benchmarking results of the latest version of the Hill-Valley Evolutionary Algorithm (HillVallEA) on the CEC2013 niching benchmark suite.

Benchmarking

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