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Evolutionary computation framework for learning from visual examples

Krawiec K.

Image Processing & Communications

2001

Vol. 7, no 3-4

85-96

This paper investigates the use of evolutionary programming for the search of hypothesis space in visual learning tasks. The general goal of the project is to elaborate human-competitive procedures for pattern discrimination by means of learning based on the training data (set of images). In particular, the topic addressed here is the comparison between the "standard" genetic programming (as defined by Koza [13] and the genetic programming extended by local optimization of solutions, so-called genetic local search. The hypothesis formulated in the paper is that genetic local search provides better solutions (i.e. classifiers with higher predictive accuracy) than the genetic search without that extension. This supposition was positively verified in an extensive comparative experiment of visual learning concerning the recognition of handwritten characters.

Improving performance of genetic local search by changing local search space topology

Jaszkiewicz A.

Foundations of Computing and Decision Sciences

1999

Vol. 24, No. 2

77-84

The paper describes a modification of genetic local search consisting in changing local search space topology. The modification may be interpreted as an extension of the idea of distance preserving crossover used in some efficient implementations of genetic local search. In the presented approach, local optimization performed after recombination does not change elements that were common to both parents. A two phases local optimization is also considered. In the second phase, local optimization is allowed to change all elements. The proposed approach is applied to travelling salesperson problem. Results of experiments reported in the paper prove that this kind of modification of the local search space topology significantly improves performance of genetic local search.