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A Continuous Approach to Genetic Programming

  • Cyril Fonlupt
  • Denis Robilliard
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6621)

Abstract

Differential Evolution (DE) is an evolutionary heuristic for continuous optimization problems. In DE, solutions are coded as vectors of floats that evolve by crossover with a combination of best and random individuals from the current generation. Experiments to apply DE to automatic programming were made recently by Veenhuis, coding full program trees as vectors of floats (Tree Based Differential Evolution or TreeDE). In this paper, we use DE to evolve linear sequences of imperative instructions, which we call Linear Differential Evolutionary Programming (LDEP). Unlike TreeDE, our heuristic provides constant management for regression problems and lessens the tree-depth constraint on the architecture of solutions. Comparisons with TreeDE and GP show that LDEP is appropriate to automatic programming.

Keywords

Regression Problem Crossover Rate Continuous Approach Destination Register Symbolic Regression 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Cyril Fonlupt
    • 1
  • Denis Robilliard
    • 1
  1. 1.LISIC — ULCOUniv Lille Nord de FranceCalais CedexFrance

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