Initialization Procedures for Multiobjective Evolutionary Approaches to the Segmentation Issue

Abstract

Evolutionary algorithms have been applied to a wide variety of domains with successful results, supported by the increase of computational resources. One of such domains is segmentation, the representation of a given curve by means of a series of linear models minimizing the representation error. This work analyzes the impact of the initialization method on the performance of a multiobjective evolutionary algorithm for this segmentation domain, comparing a random initialization with two different approaches introducing domain knowledge: a hybrid approach based on the application of a local search method and a novel method based on the analysis of the Pareto Front structure.