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Generation of New Scalarizing Functions Using Genetic Programming Read chapter Read first chapter

2020 | OriginalPaper | Chapter

Cooperative Co-Evolutionary Genetic Programming for High Dimensional Problems

Authors : Lino Rodriguez-Coayahuitl, Alicia Morales-Reyes, Hugo Jair Escalante, Carlos A. Coello Coello

Published in: Parallel Problem Solving from Nature – PPSN XVI

Publisher: Springer International Publishing

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Abstract

We propose a framework for Cooperative Co-Evolutionary Genetic Programming (CCGP) that considers co-evolution at three different abstraction levels: genotype, feature and output level. A thorough empirical evaluation is carried out on a real-world high dimensional ML problem (image denoising). Results indicate that GP’s performance is enhanced only when cooperation happens at an output level (ensemble-alike). The proposed co-evolutionary ensemble approach is compared against a canonical GP implementation and a GP customized for image processing tasks. Preliminary results show that the proposed framework obtains superior average performance in comparison to the other GP models. Our most relevant finding is the empirical evidence showing that the proposed CCGP model is a promising alternative to specialized GP implementations that require knowledge of the problem’s domain.

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previous chapter Program Synthesis in a Continuous Space Using Grammars and Variational Autoencoders
next chapter Image Feature Learning with Genetic Programming
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Metadata
Title
Cooperative Co-Evolutionary Genetic Programming for High Dimensional Problems
Authors
Lino Rodriguez-Coayahuitl
Alicia Morales-Reyes
Hugo Jair Escalante
Carlos A. Coello Coello
Copyright Year
2020
Book
Parallel Problem Solving from Nature – PPSN XVI

Print ISBN: 978-3-030-58114-5

Electronic ISBN: 978-3-030-58115-2

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-58115-2_4

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