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Statistical Genetic Programming: The Role of Diversity

  • Maryam Amir Haeri
  • Mohammad Mehdi Ebadzadeh
  • Gianluigi Folino
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 223)

Abstract

In this chapter, a new GP-based algorithm is proposed. The algorithm, named SGP (Statistical GP), exploits statistical information, i.e. mean, variance and correlation-based operators, in order to improve the GP performance. SGP incorporates new genetic operators, i.e. Correlation Based Mutation, Correlation Based Crossover, and Variance Based Editing, to drive the search process towards fitter and shorter solutions. Furthermore, this work investigates the correlation between diversity and fitness in SGP, both in terms of phenotypic and genotypic diversity. First experiments conducted on four symbolic regression problems illustrate the goodness of the approach and permits to verify the different behavior of SGP in comparison with standard GP from the point of view of the diversity and its correlation with the fitness.

Keywords

Genotypic Diversity Edit Distance Genetic Programming Model Tree Edit Distance High Phenotypic Diversity 
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 International Publishing Switzerland 2014

Authors and Affiliations

  • Maryam Amir Haeri
    • 1
  • Mohammad Mehdi Ebadzadeh
    • 1
  • Gianluigi Folino
    • 2
  1. 1.Department of Computer Engineering and Information TechnologyAmirkabir University of TechnologyTehranIran
  2. 2.ICAR-CNRRendeItaly

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