Skip to main content
main-content

Tipp

Weitere Kapitel dieses Buchs durch Wischen aufrufen

2018 | OriginalPaper | Buchkapitel

2. Biased Random-Key Genetic Progamming

verfasst von: José Fernando Gonçalves, Mauricio G. C. Resende

Erschienen in: Handbook of Heuristics

Verlag: Springer International Publishing

share
TEILEN

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.
Literatur
1.
Zurück zum Zitat Banzhaf W, Nordin P, Keller RE, Francone FD (1998) Genetic programming: an introduction. Morgan Kaufmann, San Francisco Banzhaf W, Nordin P, Keller RE, Francone FD (1998) Genetic programming: an introduction. Morgan Kaufmann, San Francisco
2.
Zurück zum Zitat Barricelli NA et al (1954) Esempi numerici di processi di evoluzione. Methodos 6(21–22): 45–68 Barricelli NA et al (1954) Esempi numerici di processi di evoluzione. Methodos 6(21–22): 45–68
3.
Zurück zum Zitat Bean JC (1994) Genetic algorithms and random keys for sequencing and optimization. ORSA J Comput 6:154–160 Bean JC (1994) Genetic algorithms and random keys for sequencing and optimization. ORSA J Comput 6:154–160
4.
Zurück zum Zitat Brameier MF, Banzhaf W (2007) Linear genetic programming. Springer, New York Brameier MF, Banzhaf W (2007) Linear genetic programming. Springer, New York
5.
Zurück zum Zitat 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 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
6.
Zurück zum Zitat Ferreira C (2001) Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst 13:87–129 Ferreira C (2001) Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst 13:87–129
7.
Zurück zum Zitat 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 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
8.
Zurück zum Zitat Ferreira C (2006) Gene expression programming: mathematical modeling by an artificial intelligence. Studies in computational intelligence. Springer, New York Ferreira C (2006) Gene expression programming: mathematical modeling by an artificial intelligence. Studies in computational intelligence. Springer, New York
9.
Zurück zum Zitat Fogel LJ (1964) On the organization of intellect. PhD thesis, UCLA Fogel LJ (1964) On the organization of intellect. PhD thesis, UCLA
10.
Zurück zum Zitat 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 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
11.
Zurück zum Zitat Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading
12.
Zurück zum Zitat Gonçalves JF, Almeida J (2002) A hybrid genetic algorithm for assembly line balancing. J Heuristics 8:629–642 Gonçalves JF, Almeida J (2002) A hybrid genetic algorithm for assembly line balancing. J Heuristics 8:629–642
13.
Zurück zum Zitat Gonçalves JF, Resende MGC (2011) Biased random-key genetic algorithms for combinatorial optimization. J Heuristics 17:487–525 Gonçalves JF, Resende MGC (2011) Biased random-key genetic algorithms for combinatorial optimization. J Heuristics 17:487–525
14.
Zurück zum Zitat 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 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
15.
Zurück zum Zitat 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 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
16.
Zurück zum Zitat 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 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
17.
Zurück zum Zitat Gonçalves J, Resende M, Toso R (2014) An experimental comparison of biased and unbiased random-key genetic algorithms. Pesquisa Operacional 34:143–164 Gonçalves J, Resende M, Toso R (2014) An experimental comparison of biased and unbiased random-key genetic algorithms. Pesquisa Operacional 34:143–164
18.
Zurück zum Zitat 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 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
19.
Zurück zum Zitat 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 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
20.
Zurück zum Zitat Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection, vol 1. MIT, Cambridge Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection, vol 1. MIT, Cambridge
21.
Zurück zum Zitat Koza JR (1994) Genetic programming II: automatic discovery of reusable subprograms. MIT, Cambridge Koza JR (1994) Genetic programming II: automatic discovery of reusable subprograms. MIT, Cambridge
22.
Zurück zum Zitat Koza JR, Bennett FH III, Andre D, Keane MA (1999) Genetic Programming III: Darwinian invention and problem solving. Morgan Kaufmann, San Francisco Koza JR, Bennett FH III, Andre D, Keane MA (1999) Genetic Programming III: Darwinian invention and problem solving. Morgan Kaufmann, San Francisco
23.
Zurück zum Zitat 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 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
24.
Zurück zum Zitat 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 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
25.
Zurück zum Zitat 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 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
Metadaten
Titel
Biased Random-Key Genetic Progamming
verfasst von
José Fernando Gonçalves
Mauricio G. C. Resende
Copyright-Jahr
2018
DOI
https://doi.org/10.1007/978-3-319-07124-4_25

Premium Partner