A state estimator design for multimodal adjustment process using genetic algorithm

Abstract

This article proposes a new kind of static state estimator based on Genetic Algorithms (GAs) in order to accomplish an estimation-based search technique for multimodal adjustment processes. To adapt GA to the estimation problem, the general procedure of the conventional GAs are modified in two aspects; first, the reproduction strategy uses two different criteria (survival cost and parent cost) for the elimination of weaker members and parent selection, and secondly, a new genetic operator based on a priori knowledge of the system is developed to generate more effective offspring solutions. The performance and applicability of the proposed estimator was investigated via three numerical examples of the multimodal adjustment system, and the results obtained show that this estimator performs very well even with the presence of a large amount of measurement noise. (C) 1998 Elsevier Science Ltd. All rights reserved.