Transform Ranking: a New Method of Fitness Scaling in Genetic Algorithms

HOPGOOD, A.A. and MIERZEJEWSKA, A. (2009). Transform Ranking: a New Method of Fitness Scaling in Genetic Algorithms. In: Research and Development in Intelligent Systems. London, Springer, 349-354.

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The first systematic evaluation of the effects of six existing forms of fitness scaling in genetic algorithms is presented alongside a new method called transform ranking. Each method has been applied to stochastic universal sampling (SUS) over a fixed number of generations. The test functions chosen were the two-dimensional Schwefel and Griewank functions. The quality of the solution was improved by applying sigma scaling, linear rank scaling, nonlinear rank scaling, probabilistic nonlinear rank scaling, and transform ranking. However, this benefit was always at a computational cost. Generic linear scaling and Boltzmann scaling were each of benefit in one fitness landscape but not the other. A new fitness scaling function, transform ranking, progresses from linear to nonlinear rank scaling during the evolution process according to a transform schedule. This new form of fitness scaling was found to be one of the two methods offering the greatest improvements in the quality of search. It provided the best improvement in the quality of search for the Griewank function, and was second only to probabilistic nonlinear rank scaling for the Schwefel function. Tournament selection, by comparison, was always the computationally cheapest option but did not necessarily find the best solutions.

Item Type: Book Section
Research Institute, Centre or Group - Does NOT include content added after October 2018: Materials and Engineering Research Institute > Modelling Research Centre > Microsystems and Machine Vision Laboratory
Identification Number:
Page Range: 349-354
Depositing User: Adrian Hopgood
Date Deposited: 30 Aug 2012 15:41
Last Modified: 18 Mar 2021 13:46

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