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Adaptive and Multilevel Metaheuristics Read chapter Read first chapter

2018 | OriginalPaper | Chapter

2. Biased Random-Key Genetic Progamming

Authors: José Fernando Gonçalves, Mauricio G. C. Resende

Published in: Handbook of Heuristics

Publisher: Springer International Publishing

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Abstract

This chapter introduces biased random-key genetic programming, a new metaheuristic for evolving programs. Each solution program is encoded as a vector of random keys, where a random key is a real number randomly generated in the continuous interval [0, 1]. A decoder maps each vector of random keys to a solution program and assigns it a measure of quality. A Program-Expression is encoded in the chromosome using a head-tail representation which is later transformed into a syntax tree using a prefix notation rule. The artificial simulated evolution of the programs is accomplished with a biased random-key genetic algorithm. Examples of the application of this approach to symbolic regression are presented.
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Metadata
Title
Biased Random-Key Genetic Progamming
Authors
José Fernando Gonçalves
Mauricio G. C. Resende
Copyright Year
2018
Book
Handbook of Heuristics

Print ISBN: 978-3-319-07123-7

Electronic ISBN: 978-3-319-07124-4

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-07124-4_25

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