Low-Budget Exploratory Landscape Analysis on Multiple Peaks Models

Kerschke Pascal, Preuss Mike, Wessing Simon, Trautmann Heike


Zusammenfassung
When selecting the best suited algorithm for an unknown optimization problem, it is useful to possess some a priori knowledge of the problem at hand. In the context of single-objective, continuous optimization problems such knowledge can be retrieved by means of Exploratory Landscape Analy\-sis (ELA), which automatically identifies properties of a landscape, e.g., the so-called funnel structures, based on an initial sample. In this paper, we extract the relevant features (for detecting funnels) out of a large set of landscape features when only given a small initial sample consisting of 50*D observations, where $D$ is the number of decision space dimensions. This is already in the range of the start population sizes of many evolutionary algorithms. The new Multiple Peaks Model Generator (MPM2) is used for training the classifier, and the approach is then very successfully validated on the Black-Box Optimization Benchmark (BBOB) and a subset of the CEC 2013 niching competition problems.



Publikationstyp
Aufsatz (Konferenz)

Begutachtet
Ja

Publikationsstatus
Veröffentlicht

Jahr
2016

Konferenz
Genetic and Evolutionary Computation Conference (GECCO '16)

Konferenzort
Denver, CO, USA

Buchtitel
Proceedings of the 18th Annual Conference on Genetic and Evolutionary Computation

Erste Seite
229

Letzte Seite
236

Sprache
Englisch

ISBN
978-1-4503-4206-3

DOI

Gesamter Text