Natural Computing

, Volume 15, Issue 3, pp 477–492

Partition based real-valued encoding scheme for evolutionary algorithms

  • Jose M. Font
  • Daniel Manrique
  • Pablo Ramos-Criado
  • David del Rio
Article

DOI: 10.1007/s11047-015-9505-6

Cite this article as:
Font, J.M., Manrique, D., Ramos-Criado, P. et al. Nat Comput (2016) 15: 477. doi:10.1007/s11047-015-9505-6
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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.

Keywords

Encoding scheme Encoding constraints Real-valued evolutionary algorithms Discrete Bayesian networks Conditional probability tables 

Mathematics Subject Classfication

68T05 (computer science, artificial intelligence, learning and adaptive systems) 68T20 (computer science, artificial intelligence, problem solving) 68Q32 (computer science, theory of computing, computational learning theory) 62F15 (statistics, parametric inference, Bayesian inference) 

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Jose M. Font
    • 1
  • Daniel Manrique
    • 2
  • Pablo Ramos-Criado
    • 2
  • David del Rio
    • 2
  1. 1.Centro Universitario de Tecnología y Arte DigitalLas RozasSpain
  2. 2.Departamento de Inteligencia ArtificialUniversidad Politécnica de MadridBoadilla del MonteSpain