Beitrag in einem Tagungsband
A Tunable Model for Multi-Objective, Epistatic, Rugged, and Neutral Fitness Landscapes
Details zur Publikation
Autor(inn)en: | Niemczyk, S.; Reichle, R.; Geihs, K.; Weise, T.; Skubch, H. |
Herausgeber: | Keijzer M, Antoniol G, Congdon CB, Deb K, Doerr B, Hansen N, Holmes JH, Hornby GS, Howard D, Kennedy J, Kumar SP, Lobo FG, Miller JF, Moore JH, Neumann F, Pelikan M, Pollack JB, Sastry K, Stanley KO, Stoica A, Talbi E, Wegener I |
Verlag: | ACM Press: New York, NY, USA |
Publikationsjahr: | 2008 |
Seitenbereich: | 795–802 |
Buchtitel: | Proceedings of the Genetic and Evolutionary Computation Conference (GECCO’08) |
DOI-Link der Erstveröffentlichung: |
Zusammenfassung, Abstract
The fitness landscape of a problem is the relation between the solution candidates and their reproduction probability. In order to understand optimization problems, it is essential to also understand the features of fitness landscapes and their interaction. In this paper we introduce a model problem that allows us to investigate many characteristics of fitness landscapes. Specifically noise, affinity for overfitting, neutrality, epistasis, multi-objectivity, and ruggedness can be independently added, removed, and fine-tuned. With this model, we contribute a useful tool for assessing optimization algorithms and parameter settings.
The fitness landscape of a problem is the relation between the solution candidates and their reproduction probability. In order to understand optimization problems, it is essential to also understand the features of fitness landscapes and their interaction. In this paper we introduce a model problem that allows us to investigate many characteristics of fitness landscapes. Specifically noise, affinity for overfitting, neutrality, epistasis, multi-objectivity, and ruggedness can be independently added, removed, and fine-tuned. With this model, we contribute a useful tool for assessing optimization algorithms and parameter settings.
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