Stephan M. Winkler
ABSTRACT
In this paper we use a formalism for estimating the structural similarity of formulas for measuring the genetic diversity among GP populations. As we show in the results section of this paper, population diversity differs a lot in the test runs depending on the selection schemata used; especially the use of strict offspring selection has a significant effect on the progress of the population's diversity.
- M. Affenzeller, S. Winkler, S. Wagner, and A. Beham. Genetic Algorithms and Genetic Programming - Modern Concepts and Practical Applications. Chapman & Hall / CRC, 2009. Google Scholar
Digital Library
- E. K. Burke, S. Gustafson, and G. Kendall. Diversity in genetic programming: An analysis of measures and correlation with fitness. IEEE Transactions on Evolutionary Computation, 8(1):47--62, 2004. Google ScholarDigital Library
- S. Wagner. Heuristic Optimization Software Systems - Modeling of Heuristic Optimization Algorithms in the HeuristicLab Software Environment. PhD thesis, Johannes Kepler University Linz, 2009.Google Scholar
- S. Winkler, M. Affenzeller, and S. Wagner. Fine-grained population diversity estimation for genetic programming based structure identification. In Proceedings of the Genetic and Evolutionary Computation Conference GECCO 2008, pages 1435--1436. Association for Computing Machinery (ACM), 2008. Google ScholarDigital Library
Index Terms
- Analysis of the effects of enhanced selection concepts for genetic programming based structure identification using fine-grained population diversity estimation
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