- author = "Alan Piszcz and Terence Soule",
- title = "Genetic Programming: Parametric Analysis of Structure Altering Mutation Techniques",
- booktitle = "Parameter Setting in Evolutionary Algorithms",
- publisher = "Springer",
- year = "2007",
- editor = "Fernando G. Lobo and Claudio F. Lima and Zbigniew Michalewicz",
- series = "Studies in Computational Intelligence",
- chapter = "7",
- pages = "143--160",
- keywords = "genetic algorithms, genetic programming, structure altering mutation technique, SAMT",
- ISBN = "3-540-69431-5",
-
URL = "
https://verso.uidaho.edu/esploro/outputs/conferencePaper/Genetic-programming-Parametric-analysis-of-structure/996631160101851",
-
DOI = "
10.1007/978-3-540-69432-8_7",
- size = "18 pages",
- abstract = "We hypothesize that the relationship between parameter settings, specifically parameters controlling mutation, and performance is non-linear in genetic programs. Genetic programming environments have few means for a priori determination of appropriate parameters values. The hypothesized nonlinear behavior of genetic programming creates difficulty in selecting parameter values for many problems. we study three structure altering mutation techniques using parametric analysis on a problem with scalable complexity. We find through parameter analysis that two of the three mutation types tested exhibit nonlinear behavior. Higher mutation rates cause a larger degree of nonlinear behavior as measured by fitness and computational effort. Characterization of the mutation techniques using parametric analysis confirms the nonlinear behavior. In addition, we propose an extension to the existing parameter setting taxonomy to include commonly used structure altering mutation attributes. Finally we show that the proportion of mutations applied to internal nodes, instead of leaf nodes, has a significant affect on performance",
-
notes = "also known as \cite{Piszcz2007}
Department of Computer Science, University of Idaho, 83844-1010, Moscow, ID, USA",
