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Biased Random-Key Genetic Progamming

  • José Fernando Gonçalves
  • Mauricio G. C. Resende
Reference work entry

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.

Keywords

Genetic programming Biased random-key genetic algorithms head-tail representation prefix notation 

Notes

Acknowledgements

The first author was supported by project PTDC/EGE-GES/117692/2010 funded by the ERDF through the Programme COMPETE and by the Portuguese Government through FCT – Foundation for Science and Technology.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • José Fernando Gonçalves
    • 1
    • 2
  • Mauricio G. C. Resende
    • 3
  1. 1.INESC TECPortoPortugal
  2. 2.Faculdade de Economia daUniversidade do PortoPortoPortugal
  3. 3.Amazon.com, Inc. and University of WashingtonSeattleUSA

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