Antonio Della Cioppa

Satellite Image Registration by Distributed Differential Evolution

In this paper a parallel software system based on Differential Evolution for the registration of images is designed, implemented and tested on a set of 2---D remotely sensed images on two problems, i.e. mosaicking and changes in time. Registration is carried out by finding the most suitable affine transformation in terms of maximization of the mutual information between the first image and the transformation of the second one, without any need for setting control points. A coarse---grained distributed version is implemented on a cluster of personal computers.

On the Structural Shape Optimization through Variational Methods and Evolutionary Algorithms

We employ the variational theory of optimal control problems and evolutionary algorithms to investigate the form finding of minimum compliance elastic structures. Mathematical properties of ground structure approaches are discussed with reference to arbitrary collections of structural elements. A numerical procedure based on a Breeder Genetic Algorithm is proposed for the shape optimization of discrete structural models. Several numerical applications are presented, showing the ability of the adopted search strategy in avoiding local optimal solutions. The proposed approach is validated against a parade of results available in the literature.

Distributed Differential Evolution for the Registration of Remotely Sensed Images

This paper deals with the design and implementation of a parallel software system based on differential evolution for the registration of images, and with its testing on two bidimensional remotely sensed images on mosaicking problem. Registration is carried out by finding the most suitable affine transformation in terms of maximization of the mutual information between the first image and the transformation of the second one, without any need for setting control points. A coarse-grained distributed version is implemented on a cluster of personal computers

A Distributed Differential Evolution Approach for Mapping in a Grid Environment

Increase in intensive applications with different computational requirements, coupled with the unification of remote and diverse resources thanks to advances in the wide-area network technologies and the low cost of components, have encouraged the development of grid computing. To exploit the promising potentials of geographically distributed resources, effective and efficient mapping algorithms are fundamental. Since the problem of optimally mapping is NP-complete, the development of evolutionary techniques to find near-optimal solutions is welcome. In this paper a distributed system based on differential evolution is designed and implemented to face the mapping problem in a grid environment aiming at reducing the degree of use of the grid resources. This system is tested on some different resource allocation scenarios

A Multiobjective Extremal Optimization Algorithm for Efficient Mapping in Grids

Extremal Optimization is proposed to map the tasks making up a user application in grid environments. To comply at the same time with minimal use of grid resources and maximal hardware reliability, a multiobjective version based on the concept of Pareto dominance is developed. The proposed mapper is tested on eight different experiments representing a suitable set of typical real-time situations.

Multiobjective differential evolution for mapping in a grid environment

Effective and efficient mapping algorithms for multisite parallel applications are fundamental to exploit the potentials of grid computing. Since the problem of optimally mapping is NP-complete, evolutionary techniques can help to find near-optimal solutions. Here a multiobjective Differential Evolution is investigated to face the mapping problem in a grid environment aiming at reducing the degree of use of the grid resources while, at the same time, maximizing Quality of Service requirements in terms of reliability. The proposed mapper is tested on different scenarios.

Speciation in evolutionary algorithms: adaptive species discovery

The use of niching methods for solving real world optimization problems is limited by the difficulty to obtain a proper setting of the speciation parameters without any a priori information about the fitness landscape. To avoid such a difficulty, we propose a novel method, called Adaptive Species Discovery, that removes the basic assumption of perfect discrimination among peaks underlying Fitness Sharing and, consequently, allows to overcome the drawbacks of the most performing sharing-based methods. This is achieved through an explicit mechanism able to discover the species in the population during the evolution. The method does not require any a priori knowledge, in that it makes no assumption about the location and the shape of the peaks, while it exploits information about the ruggedness of the fitness landscape, dynamically acquired at each generation. The proposed method has been evaluated on a set of standard functions largely adopted in the literature to assess the performance of niching methods. The experimental results show that our method has a better ability to discover and maintain all the peaks with respect to other methods proposed so far.

Parsimony doesn't mean simplicity: genetic programming for inductive inference on noisy data

A Genetic Programming algorithm based on Solomonoffs probabilistic induction is designed and used to face an Inductive Inference task, i.e., symbolic regression. To this aim, some test functions are dressed with increasing levels of noise and the algorithm is employed to denoise the resulting function and recover the starting functions. Then, the algorithm is compared against a classical parsimony-based GP. The results shows the superiority of the Solomonoff-based approach.

DETERMINISTIC AND EVOLUTIONARY EXTRACTION OF DELTA-LOGNORMAL PARAMETERS: PERFORMANCE COMPARISON

A theory, called the Kinematic Theory of Rapid Human Movement, was proposed a few years ago to analyze rapid human movements, called the Kinematic Theory of Rapid Human Movements, based on a delta-lognormal equation that globally describes the basic properties of the velocity profiles of an end-effector using seven parameters. This realistic model has been very useful for proposing original solutions to various pattern recognition problems (signature segmentation and verification, handwriting analysis and synthesis, etc.). Most of these applications rely on the use of an efficient algorithm to extract the delta-lognormal parameters from real data with the best possible fit. In this paper, we compare two such algorithms: a deterministic one, based on nonlinear regression, and a Breeder Genetic algorithm. The performance of these two algorithms and of their combinations are compared using the same artificial database, composed of analytical delta-lognormal profiles and their noisy versions (20 dB SNR). In the free-noise case, the analysis of the experimental results shows that the deterministic approach leads to better results than the evolutionary one, while under the extremely noisy conditions selected, the evolutionary approach seems to be less sensitive to noise, but is nevertheless less successful than the deterministic search.

Identification of ferrite core inductors parameters by evolutionary algorithms

This paper discusses the identification of Ferrite Core (FC) power inductors parameters in the real operating conditions relevant to Switch-Mode Power Supplies starting from experimental measurements. A novel method for parameters identification is proposed, based on Evolutionary Algorithms (EAs) and on the analysis of inductors non-linear behavior. Two EAs, the Genetic Algorithm and the Differential Evolution, are investigated and compared. The results of the proposed method are experimentally validated by means of a buck converter evaluation board.