Evaluating Genetic Algorithms through the Approximability Hierarchy

Fuente: arXiv
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Main Authors: Muñoz, Alba, Rubio, Fernando
Format: Preprint
Published: 2024
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author Muñoz, Alba
Rubio, Fernando
author_facet Muñoz, Alba
Rubio, Fernando
contents Optimization problems frequently appear in any scientific domain. Most of the times, the corresponding decision problem turns out to be NP-hard, and in these cases genetic algorithms are often used to obtain approximated solutions. However, the difficulty to approximate different NP-hard problems can vary a lot. In this paper, we analyze the usefulness of using genetic algorithms depending on the approximation class the problem belongs to. In particular, we use the standard approximability hierarchy, showing that genetic algorithms are especially useful for the most pessimistic classes of the hierarchy
format Preprint
id arxiv_https___arxiv_org_abs_2402_00444
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Evaluating Genetic Algorithms through the Approximability Hierarchy
Muñoz, Alba
Rubio, Fernando
Neural and Evolutionary Computing
Optimization problems frequently appear in any scientific domain. Most of the times, the corresponding decision problem turns out to be NP-hard, and in these cases genetic algorithms are often used to obtain approximated solutions. However, the difficulty to approximate different NP-hard problems can vary a lot. In this paper, we analyze the usefulness of using genetic algorithms depending on the approximation class the problem belongs to. In particular, we use the standard approximability hierarchy, showing that genetic algorithms are especially useful for the most pessimistic classes of the hierarchy
title Evaluating Genetic Algorithms through the Approximability Hierarchy
topic Neural and Evolutionary Computing
url https://arxiv.org/abs/2402.00444