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Handbook of Heuristics pp 23–37Cite as

Biased Random-Key Genetic Progamming

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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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References

  1. Banzhaf W, Nordin P, Keller RE, Francone FD (1998) Genetic programming: an introduction. Morgan Kaufmann, San Francisco

    Google Scholar 

  2. Barricelli NA et al (1954) Esempi numerici di processi di evoluzione. Methodos 6(21–22): 45–68

    Google Scholar 

  3. Bean JC (1994) Genetic algorithms and random keys for sequencing and optimization. ORSA J Comput 6:154–160

    Google Scholar 

  4. Brameier MF, Banzhaf W (2007) Linear genetic programming. Springer, New York

    Google Scholar 

  5. Cramer NL (1985) A representation for the adaptive generation of simple sequential programs. In: Proceedings of the first international conference on genetic algorithms, Pittsburg, pp 183–187

    Google Scholar 

  6. Ferreira C (2001) Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst 13:87–129

    Google Scholar 

  7. Ferreira C (2006) Designing neural networks using gene expression programming. In: Abraham A (ed) Applied soft computing technologies: the challenge of complexity. Springer, Berlin, pp 517–535

    Google Scholar 

  8. Ferreira C (2006) Gene expression programming: mathematical modeling by an artificial intelligence. Studies in computational intelligence. Springer, New York

    Google Scholar 

  9. Fogel LJ (1964) On the organization of intellect. PhD thesis, UCLA

    Google Scholar 

  10. Fontes DBMM, Gonçalves JF (2013) A multi-population hybrid biased random key genetic algorithm for hop-constrained trees in nonlinear cost flow networks. Optimization Letters, 7(6):1303–1324

    Google Scholar 

  11. Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading

    Google Scholar 

  12. Gonçalves JF, Almeida J (2002) A hybrid genetic algorithm for assembly line balancing. J Heuristics 8:629–642

    Google Scholar 

  13. Gonçalves JF, Resende MGC (2011) Biased random-key genetic algorithms for combinatorial optimization. J Heuristics 17:487–525

    Google Scholar 

  14. Gonçalves JF, Resende MGC (2013) A biased random-key genetic algorithm for a 2D and 3D bin packing problem. Int J Prod Econ 145:500–510

    Google Scholar 

  15. Gonçalves JF, Resende MG (2014) An extended Akers graphical method with a biased random-key genetic algorithm for job-shop scheduling. Int Trans Oper Res 21(2):215–246

    Google Scholar 

  16. Gonçalves JF, Resende MGC (2015) A biased random-key genetic algorithm for the unequal area facility layout problem. Eur J Oper Res 246(1):86–107

    Google Scholar 

  17. Gonçalves J, Resende M, Toso R (2014) An experimental comparison of biased and unbiased random-key genetic algorithms. Pesquisa Operacional 34:143–164

    Google Scholar 

  18. Gonçalves JF, Resende MGC, Costa MD (2014) A biased random-key genetic algorithm for the minimization of open stacks problem. Int Trans Oper Res. Published online 2 July 2014

    Google Scholar 

  19. Gonçalves JF, de Magalhães Mendes JJ, Resende MG (2015) The basic multi-project scheduling problem. In: Schwindt C, Zimmermann J (eds) Handbook on project management and scheduling, vol 2. Springer, Berlin, pp 667–683

    Google Scholar 

  20. Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection, vol 1. MIT, Cambridge

    Google Scholar 

  21. Koza JR (1994) Genetic programming II: automatic discovery of reusable subprograms. MIT, Cambridge

    Google Scholar 

  22. Koza JR, Bennett FH III, Andre D, Keane MA (1999) Genetic Programming III: Darwinian invention and problem solving. Morgan Kaufmann, San Francisco

    Google Scholar 

  23. Koza JR, Keane MA, Streeter MJ, Mydlowec W, Yu J, Lanza G (2003) Genetic programming IV: routine human-competitive machine intelligence. Kluwer Academic, Norwell/Dordrecht

    Google Scholar 

  24. Poli R (1997) Evolution of graph-like programs with parallel distributed genetic programming. In: Proceedings of the 7th international conference on genetic algorithms (ICGA), East Lansing, pp 346–353

    Google Scholar 

  25. Spears WM, DeJong KA (1991) On the virtues of parameterized uniform crossover. In: Proceedings of the fourth international conference on genetic algorithms, San Diego, pp 230–236

    Google Scholar 

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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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Authors and Affiliations

  1. INESC TEC, Porto, Portugal

    José Fernando Gonçalves

  2. Faculdade de Economia da, Universidade do Porto, Porto, Portugal

    José Fernando Gonçalves

  3. Amazon.com, Inc. and University of Washington, Seattle, WA, USA

    Mauricio G. C. Resende

Authors
  1. José Fernando Gonçalves
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  2. Mauricio G. C. Resende
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Corresponding author

Correspondence to José Fernando Gonçalves .

Editor information

Editors and Affiliations

  1. Statistics and Operations Research Department, University of Valencia, Valencia, Spain

    Rafael Martí

  2. Department of Industrial and Systems Engineering, University of Florida, Gainesville, Florida, USA

    Panos M. Pardalos

  3. Amazon.com, Inc. and University of Washington, Seattle, Washington, USA

    Mauricio G. C. Resende

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Gonçalves, J.F., Resende, M.G.C. (2018). Biased Random-Key Genetic Progamming. In: Martí, R., Pardalos, P., Resende, M. (eds) Handbook of Heuristics. Springer, Cham. https://doi.org/10.1007/978-3-319-07124-4_25

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