Studying the Relationship Between Crossover Features and Performance on MNK-Landscapes Using Regression Models

Teruhisa Nakashima, Hernán Aguirre, Kiyoshi Tanaka

2024

Abstract

Crossover is a key component of evolutionary algorithms and has been the focus of numerous studies. Its effectiveness depends on the operator’s properties to mix information, the specific characteristics of the problem, and the diversity of the population, influenced by the dynamics of the algorithm. This study focuses on binary representations and introduces a method to examine the relationship between crossover features and the performance of a multi-objective evolutionary algorithm on problem subclasses with random and neighbor patterns of variable interactions. The aim is to identify the crossover features relevant to performance in each problem subclass through regression models.

Paper Citation

in Harvard Style

Nakashima T., Aguirre H. and Tanaka K. (2024). Studying the Relationship Between Crossover Features and Performance on MNK-Landscapes Using Regression Models. In Proceedings of the 16th International Joint Conference on Computational Intelligence - Volume 1: ECTA; ISBN 978-989-758-721-4, SciTePress, pages 84-95. DOI: 10.5220/0012941200003837

in Bibtex Style

@conference{ecta24,
author={Teruhisa Nakashima and Hernán Aguirre and Kiyoshi Tanaka},
title={Studying the Relationship Between Crossover Features and Performance on MNK-Landscapes Using Regression Models},
booktitle={Proceedings of the 16th International Joint Conference on Computational Intelligence - Volume 1: ECTA},
year={2024},
pages={84-95},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012941200003837},
isbn={978-989-758-721-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computational Intelligence - Volume 1: ECTA
TI - Studying the Relationship Between Crossover Features and Performance on MNK-Landscapes Using Regression Models
SN - 978-989-758-721-4
AU - Nakashima T.
AU - Aguirre H.
AU - Tanaka K.
PY - 2024
SP - 84
EP - 95
DO - 10.5220/0012941200003837
PB - SciTePress