Introduction of a sectioned genetic algorithm for large scale problems

Abstract

The sectioned genetic algorithm (hereafter denoted as sectioned GA), which is presented in this paper, represents a modification of the standard GA and deals with large scale problems (i.e. problems involving pattern spaces with high dimensionalities). Instead of increasing the size of the population searching the pattern space when the problem dimensionality increases, the sectioned GA approach divides each individual into smaller parts (sections) and subsequently applies the genetic operators on each of these parts. Results from the application of sectioned GA on the problem of automatic morphological analysis are also presented in this article. Morphological analysis is by nature a large scale problem since a great number of words need to be segmented into stems and suffixes. The proposed system improves the segmentation accuracy substantially in comparison to standard GA algorithms.