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Empirical problem decomposition — the key to the evolutionary effectiveness in solving a large-scale non-binary discrete real-world problem

  • Published:2021-12 Issue: Volume:113 Page:107864
  • ISSN:1568-4946
  • Container-title:Applied Soft Computing
  • language:en
  • Short-container-title:Applied Soft Computing

Author:

Przewozniczek Michal WitoldORCID,Komarnicki Marcin Michal

Publisher

Elsevier BV

Subject

Software

Reference55 articles.

1. Leveraging conditional linkage models in gray-box optimization with the real-valued gene-pool optimal mixing evolutionary algorithm;Bouter,2020

2. Expanding from discrete cartesian to permutation gene-pool optimal mixing evolutionary algorithms;Bosman,2016

3. The transformation of the k-shortest steiner trees search problem into binary dynamic problem for effective evolutionary methods application;Przewoźniczek;Inform. Sci.,2019

4. Modeling building-block interdependency;Watson,1998

5. Hierarchical problem solving with the linkage tree genetic algorithm;Thierens,2013

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3. To slide or not to slide? Moving along fitness levels and preserving the gene subsets diversity in modern evolutionary computation;Proceedings of the Genetic and Evolutionary Computation Conference;2023-07-12

4. Convergent Fuzzy Cognitive Modelling of Regional Youth Policy Strategy;Proceedings of Eighth International Congress on Information and Communication Technology;2023

5. Empirical linkage learning for non-binary discrete search spaces in the optimization of a large-scale real-world problem;Proceedings of the Genetic and Evolutionary Computation Conference Companion;2022-07-09
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