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

Published in:

2018 | OriginalPaper | Chapter

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

Published in: Handbook of Heuristics

Publisher: Springer International Publishing

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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