Evolution tracking in genetic programming

Publikation: Beitrag in Buch/Bericht/TagungsbandKonferenzbeitragBegutachtung

1 Zitat (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.

OriginalspracheEnglisch
Titel24th European Modeling and Simulation Symposium, EMSS 2012
Seiten362-367
Seitenumfang6
PublikationsstatusVeröffentlicht - 2012
Veranstaltung24th European Modeling and Simulation Symposium, EMSS 2012 - Vienna, Österreich
Dauer: 19 Sep 201221 Sep 2012

Publikationsreihe

Name24th European Modeling and Simulation Symposium, EMSS 2012

Konferenz

Konferenz24th European Modeling and Simulation Symposium, EMSS 2012
Land/GebietÖsterreich
OrtVienna
Zeitraum19.09.201221.09.2012

Fingerprint

Untersuchen Sie die Forschungsthemen von „Evolution tracking in genetic programming“. Zusammen bilden sie einen einzigartigen Fingerprint.

Zitieren