WFRF:(Frank D)
Search: WFRF:(Frank D) > (2005-2009) > Dynamics and Perfor...
- 28 of 90
- Previous record
- Next record
- To hitlist
Dynamics and Performance of a Linear Genetic Programming System
-
- Francone, Frank D., 1951 (author)
- Chalmers tekniska högskola,Chalmers University of Technology
- (creator_code:org_t)
- 2009
- English.
- Licentiate thesis (other academic/artistic)
Abstract
Subject headings
Close
- Genetic Programming (“GP”) is a machine learning algorithm. Typically, Genetic Programming is a supervised learning algorithm, which trains on labeled training examplesprovided by the user. The solution output by GP maps known attributes to the known labels.Genetic Programming is distinctive from other machine learning algorithms in that its output is typically a computer program—hence “Genetic Programming.” The GP systemdocumented here conducts learning with a series of very simple selection and transformation steps—modeled loosely on biological evolution—repeated over-and-over on a population of evolving computer programs. The selection step mimics natural selection. The transformation steps—crossover and mutation—loosely mimic biological eucaryotic reproduction. Although the individual steps are simple, the dynamics of a GP run are complex.This thesis traces key research elements in the design of a widely-used GP system. It also presents empirical comparisons of the GP system that resulted from these designelements to other leading machine learning algorithms. Each of the issues addressed in this thesis touches on what was, at the time of publication, a key—and not well understood—issue regarding the dynamics and behavior of GP runs. In particular, the design issues addressed here are threefold: (1) The emergence in GP runs of “introns” or “code bloat.” Introns in GP are segments of code that have no effect on the output of the program in which they appear. Introns are an emergent phenomenon in GP. This thesis reports results that support the hypothesis that Introns emerge as a means of protecting evolving programs against the destructive effect of the traditional GP crossover transformationoperator. (2) Mutation in biological reproduction is rare and usually destructive. However, we present results which establish that, in GP, using the mutation transformationoperator at a high level, generates better and more robust evolved programs than using the mutation transformation operator at levels more typical of biological evolution.(3) Finally, we return to the GP crossover operator and present results that suggest that a “homologous” crossover operator produces better and more robust results than thetraditional GP crossover operator. The GP system that resulted from the above research has been publicly available since 1998. It has been extensively tested and compared to other machine learning paradigms. This thesis presents results that demonstrate that the resulting GP system produces consistently high-quality and robust solutions when compared to Vapnick statistical regression, decision trees, and neural networks over a wide range of problem domains and problem types.
Subject headings
- NATURVETENSKAP -- Fysik (hsv//swe)
- NATURAL SCIENCES -- Physical Sciences (hsv//eng)
Keyword
- Mutation
- Genetic Programming
- Homologous Crossover.
- Crossover
- Introns
- Linear Genetic Programming
- Code Bloat
Publication and Content Type
- lic (subject category)
- vet (subject category)
Find in a library
- Dynamics and Performance of a Linear Genetic Programming System (Search the publication in LIBRIS)
To the university's database
- 28 of 90
- Previous record
- Next record
- To hitlist
Find more in SwePub
- By the author/editor
- Francone, Frank ...
- About the subject
-
- NATURAL SCIENCES
- NATURAL SCIENCES
- and Physical Science ...
- By the university
- Chalmers University of Technology
Search outside SwePub
- Extend your search to:
- Google Book Search
- Google Scholar
Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.
- Copyright © LIBRIS - National Library Systems
- LIBRIS.kb.se
