- author = "Alan Piszcz",
- title = "Towards understanding the Correlation of Problem Difficulty and Parameter Sensitivity in Genetic Programming",
- school = "Department of Computer Science, University of Idaho",
- year = "2006",
- address = "Moscow, ID, USA",
- month = nov,
- keywords = "genetic algorithms, genetic programming",
-
URL = "
https://www.uidaho.edu/engr/departments/cs/research/theses",
-
URL = "http://search.proquest.com/docview/305328322",
- size = "145 pages",
- abstract = "Genetic programming is a machine-learning technique inspired by biological evolution to synthesize programs for a given computational task. It can produce results that equal or exceed human-designed solutions in areas such as electronic circuits, electronic filters, electronic control systems, optics, antennas and planning problems. Genetic programming algorithms use control parameters to modify the simulation environment. A significant problem confronting researchers is determining the appropriate set of values such as population size and mutation frequency. The choices for parameter settings vary from default values, to experience or rule of thumb techniques discovered by the research community. Inappropriate parameter settings often result in no solutions. We examine mutation techniques, population size and associated parameter values to explain how each inhibits or promotes the evolution of solutions with genetic programming. The experiments analyse the performance of the genetic program using parameter sweeps for mutation frequency and population size. Analysis of the mutation experiments result in three general findings. First, the selection method for individuals to mutate (e.g. the best, the worst, the average, etc.) is a critical factor. Second, we show non-linear effects on fitness from three structure altering mutation techniques. All three structure altering techniques show a non-linear relationship between the rate of mutation and the performance of the GP. Third, a correlation exists between problem complexity and mutation frequency. The results of the population experiments led to four important, specific discoveries. First, the determination of near optimal parameter settings leads to an improvement in computational efficiency. Second, improvement in the computational efficiency that is obtained when optimal or near optimal parameter values are used varies with problem complexity. Third, problems with low complexity show significant increases in the number of acceptable solutions with changes in population size. Fourth, the optimal population size narrows as problem difficulty increases. The employment of mutation in GP research papers needs improvement. We introduce an expanded mutation description scheme to improve the role of mutation in GP research.",
-
notes = "Supervisor: Terrance Soule
UMI Microform 3250627",
