A multiple expression alignment framework for genetic programming

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Alignment in the error space is a recent idea to exploit semantic awareness in genetic programming. In a previous contribution, the concepts of optimally aligned and optimally coplanar individuals were introduced, and it was shown that given optimally aligned, or optimally coplanar, individuals, it is possible to construct a globally optimal solution analytically. As a consequence, genetic programming methods, aimed at searching for optimally aligned, or optimally coplanar, individuals were introduced. In this paper, we critically discuss those methods, analyzing their major limitations and we propose new genetic programming systems aimed at overcoming those limitations. The presented experimental results, conducted on four real-life symbolic regression problems, show that the proposed algorithms outperform not only the existing methods based on the concept of alignment in the error space, but also geometric semantic genetic programming and standard genetic programming.

Original languageEnglish
Title of host publicationGenetic Programming
Subtitle of host publication21st European Conference, EuroGP 2018, Proceedings
EditorsMauro Castelli, Lukas Sekanina, Mengjie Zhang, Stefano Cagnoni, Pablo García-Sánchez
PublisherSpringer Verlag
Pages166-183
Number of pages18
Volume10781 LNCS
ISBN (Print)9783319775524
DOIs
Publication statusPublished - 1 Jan 2018
Event21st European Conference on Genetic Programming, EuroGP 2018 - Parma, Italy
Duration: 4 Apr 20186 Apr 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10781 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference21st European Conference on Genetic Programming, EuroGP 2018
CountryItaly
CityParma
Period4/04/186/04/18

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