Symbolic regression using tabu search in a neighborhood of semantically similar solutions

Publikation: Beitrag in Buch/Bericht/TagungsbandKonferenzbeitragBegutachtung

1 Zitat (Scopus)

Abstract

In this paper we describe for the first time a tabu search algorithm for symbolic regression. The novel contribution presented in this work is the idea to use a metric for semantic similarity to generate moves in such a way that branches are only replaced with semantically similar branches. In symbolic regression separate parts of the solution are linked strongly; often a small random change of one part might disrupt a link and thus, can completely change the semantics of the solution. We hypothesize that by introducing the semantic similarity constraint, the fitness landscape for tabu search becomes smoother as each move can only change the fitness of the solution slightly. However, empirical evaluation on a set of simple benchmark instances shows that the approach described in this paper does not perform as well as genetic programming with offspring selection and there is no big difference between random and semantic move generation.

OriginalspracheEnglisch
Titel24th European Modeling and Simulation Symposium, EMSS 2012
Seiten379-384
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

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