Evolutionary Computation Framework for Learning from Visual Examples 1
Krzysztof Krawiec · 2001
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.