Breadcrumb

 
 

Incorporating the simplicity first methodology into a machine learning genetic algorithm

Title:

Incorporating the simplicity first methodology into a machine learning genetic algorithm

Winikoff, Steven M (1999) Incorporating the simplicity first methodology into a machine learning genetic algorithm. Masters thesis, Concordia University.

[img]
Preview
PDF
6Mb

Abstract

The classical genetic algorithm provides a powerful yet domain-independent tool for concept learning. However, in general, learning systems based on the genetic algorithm generally do not perform as well as symbolic learning algorithms. Robert Holte's symbolic learning algorithm 1R demonstrated that simple rules can perform well in non-trivial learning problems, and inspired an approach to machine learning which Holte termed "simplicity first research methodology". A system called ELGAR is proposed, constructed and evaluated in order to investigate the properties of concept learning using the genetic algorithm. A hybrid algorithm is then developed and implemented which integrates the genetic algorithm in ELGAR with the "simplicity first" approach, resulting in a concept learning system that outperforms both 1R and the purely genetic version of ELGAR.

Divisions:Concordia University > Faculty of Engineering and Computer Science > Computer Science and Software Engineering
Item Type:Thesis (Masters)
Authors:Winikoff, Steven M
Pagination:x, 168 leaves ; 29 cm.
Institution:Concordia University
Degree Name:Theses (M.Comp.Sc.)
Program:Computer Science and Software Engineering
Date:1999
Thesis Supervisor(s):Shinghal, Rajjan
ID Code:688
Deposited By:Concordia University Libraries
Deposited On:27 Aug 2009 13:13
Last Modified:08 Dec 2010 10:16
Related URLs:
All items in Spectrum are protected by copyright, with all rights reserved. The use of items is governed by Spectrum's terms of access.

Repository Staff Only: item control page

Document Downloads

More statistics for this item...

Concordia University - Footer