“Incremental” Evaluation for Genetic Crossover

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Abstract

Incremental evaluation is a big advantage for trajectory-based optimization algorithms. Previously, the application of similar ideas to crossover-based algorithms, such as genetic algorithms did not seem appealing as the expected benefit would be marginal. We propose the use of an immutable data structure that stores partial evaluation results inside of the solution representation, and composing new solution from parts of previously evaluated candidates, which can speed up re-evaluation. The application of this idea to the knapsack problem shows promising results hinting at logarithmic complexity in case all genetic operators can be adapted accordingly.

Original languageEnglish
Title of host publicationComputer Aided Systems Theory – EUROCAST 2019 - 17th International Conference, Revised Selected Papers
EditorsRoberto Moreno-Díaz, Alexis Quesada-Arencibia, Franz Pichler
PublisherSpringer
Pages396-404
Number of pages9
ISBN (Print)9783030450922
DOIs
Publication statusPublished - 2020
Event17th International Conference on Computer Aided Systems Theory, EUROCAST 2019 - Las Palmas de Gran Canaria, Spain
Duration: 17 Feb 201922 Feb 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12013 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th International Conference on Computer Aided Systems Theory, EUROCAST 2019
CountrySpain
CityLas Palmas de Gran Canaria
Period17.02.201922.02.2019

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