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Partition based real-valued encoding scheme for evolutionary algorithms

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Abstract

Encoding feasible solutions is one of the most important aspects to be taken into account in the field of evolutionary computation in order to solve search or optimization problems. This paper proposes a new encoding scheme for real-coded evolutionary algorithms. It is called partition based encoding scheme, and satisfies two restrictions. Firstly, each of the components of a decoded vector that conforms a candidate solution to a problem at hand belongs to a predefined interval. Secondly, the sum of the components of each of these decoded vectors is always equal to a predefined constant. The proposed encoding scheme inherently guarantees these constraints for all the individuals that are generated within the evolution process as a consequence of applying the genetic operators. Partition based encoding scheme is successfully applied to learning conditional probability tables for a given discrete Bayesian network topology, where each row of the tables must exactly add up to one, and the components of each row belong to the interval [0,1] as they are probability values. The results given by the proposed encoding system for this learning problem is compared to a deterministic algorithm and another evolutionary approach. Better results are shown in terms of accuracy with respect to the former one, and accuracy and convergence speed with respect to the later one.

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References

  • Abdul-Rahman O, Munetomo M, Akama K (2011) An improved binary-real coded genetic algorithm for real parameter optimization. In: Proceedings of third world congress on nature and biologically inspired computing, Salamanca, Spain, pp 149–156

  • Barrios D, Manrique D, Carrascal A (2003) Optimisation with real-coded genetic algorithms based on mathematical morphology. Int J Comput Math 80(3):275–293

    Article  MathSciNet  MATH  Google Scholar 

  • Beaser E, Schwartz JK, Bell CB, Solomon EI (2011) Hybrid genetic algorithm with an adaptive penalty function for fitting multimodal experimental data: application to exchange-coupled non-kramers binuclear iron active sites. J Chem Inf Model 51(9):2164–2173

    Article  Google Scholar 

  • Chomsky N (1956) Three models for the description of language. IEEE Trans Inform Theory 2(3):113–124

    Article  MATH  Google Scholar 

  • Choubey NS (2010) A novel encoding scheme for traveling tournament problem using genetic algorithm. Int J Comput Appl 2:79–82

    Google Scholar 

  • Coello CA (2008) Constraint-handling techniques used with evolutionary algorithms. In: Proceedings of the 2008 GECCO conference companion on genetic and evolutionary computation, Atlanta, USA, pp 2445–2466

  • Couchet J, Manrique D, Ríos J, Rodriguez-Paton A (2007) Crossover and mutation operators for grammar-guided genetic programming. Soft Comput 11(10):943–955

    Article  Google Scholar 

  • De Jong KA (2006) Evolutionary computation: a unified approach. MIT press, Cambridge.

    MATH  Google Scholar 

  • Dong L, Liu G, Yuan S, Li Y, Li Z (2007) Classifier learning algorithm based on genetic algorithms. In: Proceedings of ICICIC’07 second international conference on innovative computing, information and control pp 126–129

  • Font JM, Manrique D, Ríos J (2010) Evolutionary construction and adaptation of intelligent systems. Exp Syst Appl 37(12):7711–7720

    Article  Google Scholar 

  • Font JM, Manrique D, Pascua E (2011) Grammar-guided evolutionary construction of bayesian networks. In: Foundations on natural and artificial computation. Lecture Notes in Computer Science 6686:60–69

  • Garcia-Arnau M, Manrique D, Ríos J, Rodríguez-Paton A (2007) Initialization method for grammar-guided genetic programming. Knowl Based Syst 20(2):127–133

    Article  Google Scholar 

  • Griner R, Su X, Shen B, Zhou W (2005) Structural extension to logistic regression: discriminative parameter learning of belief net classifiers. Mach Learn 59(3):297–322

    Article  MATH  Google Scholar 

  • Kari L, Rozenberg G (2008) The many facets of natural computing. Commun ACM 51(10):72–83

    Article  Google Scholar 

  • Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge, MA

    MATH  Google Scholar 

  • Larrañaga P, Poza M, Yurramendi Y, Murga RH, Kuijpers CM (1996) Structure learning of Bayesian networks by genetic algorithms: a performance analysis of control parameters. IEEE Trans Pattern Anal 18(9):912–926

    Article  Google Scholar 

  • Lichman M (2013) UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science. http://archive.ics.uci.edu/ml

  • Loiacono D, Cardamone L, Lanzi P (2011) Automatic track generation for high-end racing games using evolutionary computation. IEEE Trans Comp Intel AI 3(3):245–259

    Google Scholar 

  • Manning CD, Raghavan P, Schütze H (2008) Introduction to information retrieval. Cambridge University Press, Cambridge

    Book  MATH  Google Scholar 

  • Nowling RJ, Mauch H (2011) Priority encoding scheme for solving permutation and constraint problems with genetic algorithms and simulated annealing. In: Proceedings of the eighth international conference on information technology: New Generations, ITNG 2011, Las Vegas, USA, pp 810–815

  • Russel SJ, Norvig P (2009) Artificial intelligence: a modern approach, 3rd edn. Prentice Hall, Englewood Cliffs, NJ

    Google Scholar 

  • Simon D (2013) Evolutionary optimization algorithms. Wiley, Hoboken

    Google Scholar 

  • Su J, Zhang H, Ling CX, Matwin S (2008) Discriminative parameter learning for bayesian networks. In: Proceedings of the 25th international conference on machine learning, Helsinki, Finland, pp 1016–1023

  • The University of Waikato (2014) Weka http://www.cs.waikato.ac.nz/ml/weka. Accessed July 2014

  • Whigham P (1995) Grammatically-based genetic programming. In: Proceedings of the workshop on genetic programming: from theory to real-world applications 16, pp 33–41

  • Yu X, Gen M (2010) Introduction to evolutionary algorithms. Springer, London

    Book  MATH  Google Scholar 

Download references

Acknowledgments

Authors acknowledge the resources offered by the UCI repository (CA: University of California, School of Information and Computer Science 2014) and the Weka software (The University of Waikato 2014).

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Authors and Affiliations

  1. Centro Universitario de Tecnología y Arte Digital, Calle Playa de Liencres, 2, 28290, Las Rozas, Madrid, Spain

    Jose M. Font

  2. Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, 28660, Boadilla del Monte, Madrid, Spain

    Daniel Manrique, Pablo Ramos-Criado & David del Rio

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  1. Jose M. Font
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  2. Daniel Manrique
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  3. Pablo Ramos-Criado
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  4. David del Rio
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Correspondence to Daniel Manrique.

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Font, J.M., Manrique, D., Ramos-Criado, P. et al. Partition based real-valued encoding scheme for evolutionary algorithms. Nat Comput 15, 477–492 (2016). https://doi.org/10.1007/s11047-015-9505-6

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  • DOI: https://doi.org/10.1007/s11047-015-9505-6

Keywords

Mathematics Subject Classfication