Empirical problem decomposition — the key to the evolutionary effectiveness in solving a large-scale non-binary discrete real-world problem-Reference-Cited by-同舟云学术

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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 Witold^ORCID,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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1. 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

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

3. 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

4. On turning black - into dark gray-optimization with the direct empirical linkage discovery and partition crossover;Proceedings of the Genetic and Evolutionary Computation Conference;2022-07-08

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