Towards identifying salient patterns in genetic programming individuals

  • András Joó

Student thesis: Doctoral ThesisDoctor of Philosophy


This thesis addresses the problem of offline identification of salient patterns in genetic programming individuals. It discusses the main issues related to automatic pattern identification systems, namely that these (a) should help in understanding the final solutions of the evolutionary run, (b) should give insight into the course of evolution and (c) should be helpful in optimizing future runs. Moreover, it proposes an algorithm, Extended Pattern Growing Algorithm ([E]PGA) to extract, filter and sort the identified patterns so that these fulfill as many as possible of the following criteria: (a) they are representative for the evolutionary run and/or search space, (b) they are human-friendly and (c) their numbers are within reasonable limits. The results are demonstrated on six problems from different domains.
Date of Award12 Jun 2010
Original languageEnglish
SupervisorJuan Neirotti (Supervisor)


  • genetic programming
  • tree mining
  • data mining.

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