Genetic Programming for Object Detection : a Two-Phase Approach with an Improved Fitness Function

This paper describes two innovations that improve the efficiency and effectiveness of a genetic programming approach to object detection problems. The approach uses genetic programming to construct object detection programs that are applied, in a moving window fashion, to the large images to locate the objects of interest. The first innovation is to break the GP search into two phases with the first phase applied to a selected subset of the training data, and a simplified fitness function. The second phase is initialised with the programs from the first phase, and uses the full set of training data with a complete fitness function to construct the final detection programs. The second innovation is to add a program size component to the fitness function. This approach is examined and compared with a neural network approach on three object detection problems of increasing difficulty. The results suggest that the innovations increase both the effectiveness and the efficiency of the genetic programming search, and also that the genetic programming approach outperforms a neural network approach for the most difficult data set in terms of the object detection accuracy

Training Neural Networks by means of Genetic Algorithms Working on Very Long Chromosomes

In the neural network / genetic algorithm community, rather limited success in the training of neural networks by genetic algorithms has been reported. In a paper by Whitley (1991), he claims that, due to "the multiple representations problem", genetic algorithms will not effectively be able to train multilayer perceptrons, whoes chromosomal representation of its weights exceeds 300 bit's. In the following paper, by use of a "real-life problem", known to be non-trivial, and by a comparison with "classic" neural net training methods, I will try to show, that the modest success of applying genetic algorithms to the training of perceptrons, is caused not so much by the "multiple representations problem" as by the fact that problem-specific knowledge available is often ignored, thus making the problem unnecessarily tough for the genetic algorithm to solve. Special success is obtained by the use of a new fitness function, which takes into account the fact, that the search performed by a gen..

Intelligent state observers : proposals

Orientador: Celso Pascoli BotturaDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de ComputaçãoResumo: O problema de se observar o estado do sistema nasce da necessidade de se controlar plantas cujo estado não se pode medir. Convencionalmente são utilizados observadores baseados em modelos dinâmicos para resolver esse problema, através da construção de estimativas do estado desconhecido. Nesta tese, é abordada uma solução alternativa para este problema. Várias propostas de observadores inteligentes, são realizadas com base em mapeamentos entre subespaços gerados pelas equações que regem a dinâmica não linear do sistema, utilizando várias técnicas de inteligência computacional, capazes de apresentar estimativas do estado do sistema dinâmico através de observadores que não apresentam um comportamento dinâmico com relação ao tempo.Abstract: The state observer problem arises from the necessity of controlling plants whose state can not be measured. Usually dynamic model based observers are used to solve this problem, for estimating unknown state. In this thesis an alternative solution for this problem is presented. Various proposals for intelligent observers are made based on mappings between subspaces generated by the equations describing the nonlinear dynamics of the system, using various computational intelligence techniques able to present state estimates for the dynamic system through nonlinear observers that do not exhibit a dynamic behavior with respect to time.MestradoAutomaçãoMestre em Engenharia Elétric