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Partition based real-valued encoding scheme for evolutionary algorithms


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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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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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).

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  • 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)