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Erschienen in:
Using GP Is NEAT: Evolving Compositional Pattern Production Functions Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

A Multiple Expression Alignment Framework for Genetic Programming

verfasst von : Leonardo Vanneschi, Kristen Scott, Mauro Castelli

Erschienen in: Genetic Programming

Verlag: Springer International Publishing

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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.

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Vorheriges Kapitel Towards in Vivo Genetic Programming: Evolving Boolean Networks to Determine Cell States
Nächstes Kapitel Multi-objective Evolution of Ultra-Fast General-Purpose Hash Functions
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Metadaten
Titel
A Multiple Expression Alignment Framework for Genetic Programming
verfasst von
Leonardo Vanneschi
Kristen Scott
Mauro Castelli
Copyright-Jahr
2018
Buch
Genetic Programming

Print ISBN: 978-3-319-77552-4

Electronic ISBN: 978-3-319-77553-1

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-77553-1_11