Biased Random-Key Genetic Progamming

José Fernando Gonçalves () and Mauricio G. C. Resende ()
Additional contact information
José Fernando Gonçalves: INESC TEC
Mauricio G. C. Resende: Amazon.com, Inc. and University of Washington

Chapter 2 in Handbook of Heuristics, 2018, pp 23-37 from Springer

Abstract: 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 (search for similar items in EconPapers)
Date: 2018
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-07124-4_25

Ordering information: This item can be ordered from
http://www.springer.com/9783319071244

DOI: 10.1007/978-3-319-07124-4_25

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().