Evolution tracking in genetic programming

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1 Citation (Scopus)

Abstract

Much effort has been put into understanding the artificial evolutionary dynamics within genetic programming (GP). However, the details are yet unclear so far, as to which elements make GP so powerful. This paper presents an attempt to study the evolution of a population of computer programs using HeuristicLab. A newly developed methodology for recording heredity information, based on a general conceptual framework of evolution, is employed for the analysis of algorithm behavior on a symbolic regression benchmark problem. In our example, we find the complex interplay between selection and crossover to be the cause for size increase in the population, as the average amount of genetic information transmitted from parents to offspring remains constant and independent of run constraints (i.e., tree size and depth limits). Empirical results reveal many interesting details and confirm the validity and generality of our approach, as a tool for understanding the complex aspects of GP.

Original languageEnglish
Title of host publication24th European Modeling and Simulation Symposium, EMSS 2012
Pages362-367
Number of pages6
Publication statusPublished - 2012
Event24th European Modeling and Simulation Symposium, EMSS 2012 - Vienna, Austria
Duration: 19 Sep 201221 Sep 2012

Publication series

Name24th European Modeling and Simulation Symposium, EMSS 2012

Conference

Conference24th European Modeling and Simulation Symposium, EMSS 2012
CountryAustria
CityVienna
Period19.09.201221.09.2012

Keywords

  • Bloat
  • Evolutionary dynamics
  • Genetic programming
  • Introns
  • Population diversity
  • Schema theory
  • Tree fragments

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