Ernesto Tarantino

Facing classification problems with Particle Swarm Optimization

The use of Particle Swarm Optimization, a heuristic optimization technique based on the concept of swarm, is described to face the problem of classification of instances in multiclass databases. Three different fitness functions are taken into account, resulting in three versions being investigated. Their performance is contrasted on 13 typical test databases. The resulting best version is then compared against other nine classification techniques well known in literature. Results show the competitiveness of Particle Swarm Optimization. In particular, it turns out to be the best on 3 out of the 13 challenged problems.

Discovering interesting classification rules with genetic programming

Abstract Data mining deals with the problem of discovering novel and interesting knowledge from large amount of data. This problem is often performed heuristically when the extraction of patterns is difficult using standard query mechanisms or classical statistical methods. In this paper a genetic programming framework, capable of performing an automatic discovery of classification rules easily comprehensible by humans, is presented. A comparison with the results achieved by other techniques on a classical benchmark set is carried out. Furthermore, some of the obtained rules are shown and the most discriminating variables are evidenced.

Differential Evolution as a viable tool for satellite image registration

A software system grounded on Differential Evolution to automatically register multiview and multitemporal images is designed, implemented and tested through a set of 2D satellite images on two problems, i.e. mosaicking and changes in time. Registration is effected by looking for the best affine transformation in terms of maximization of the mutual information between the first image and the transformation of the second one, and no control points are needed in this approach. This method is compared against five widely available tools, and its effectiveness is shown.

Mutation-based genetic algorithm: performance evaluation

Abstract The role of mutation has been frequently underestimated in the field of Evolutionary Computation. Moreover only little work has been done by researchers on mutations other than the classical point mutation. In fact, current versions of Genetic Algorithms (GAs) make use of this kind of mutation only, in spite of the existence in nature of many different forms of mutations. In this paper, we try to address these issues starting from the definition of two nature-based mutations, i.e. the frame-shift and the translocation. These mutation operators are applied to the solution of several test functions without making use of crossover. A comparison with the results achieved by classical crossover-based GAs, both sequential and parallel, shows the effectiveness of such operators.

Biological invasion-inspired migration in distributed evolutionary algorithms

Migration strategy plays an important role in designing effective distributed evolutionary algorithms. In this work, a novel migration model inspired to the phenomenon known as biological invasion is devised. The migration strategy is implemented through a multistage process involving invading subpopulations and their competition with native individuals. Such a general approach is used within a stepping-stone parallel model adopting Differential Evolution as the local algorithm. The resulting distributed algorithm is evaluated on a wide set of classical test functions against a large number of sequential and other distributed versions of Differential Evolution available in literature. The findings show that, in most of the cases, the proposed algorithm is able to achieve better performance in terms of both solution quality and convergence rate.

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.

Evolutionary Neural Networks for Nonlinear Dynamics Modeling

In this paper the evolutionary design of a neural network model for predicting nonlinear systems behavior is discussed. In particular, the Breeder Genetic Algorithms are considered to provide the optimal set of synaptic weights of the network. The feasibility of the neural model proposed is demonstrated by predicting the Mackey-Glass time series. A comparison with Genetic Algorithms and Back Propagation learning technique is performed.

Extremal Optimization applied to load balancing in execution of distributed programs

The paper describes methods for using Extremal Optimization (EO) for processor load balancing during execution of distributed applications. A load balancing algorithm for clusters of multicore processors is presented and discussed. In this algorithm the EO approach is used to periodically detect the best tasks as candidates for migration and for a guided selection of the best computing nodes to receive the migrating tasks. To decrease the complexity of selection for migration, the embedded EO algorithm assumes a two-step stochastic selection during the solution improvement based on two separate fitness functions. The functions are based on specific models which estimate relations between the programs and the executive hardware. The proposed load balancing algorithm is assessed by experiments with simulated load balancing of distributed program graphs. The algorithm is compared against a greedy fully deterministic approach, a genetic algorithm and an EO-based algorithm with random placement of migrated tasks.

An evolutionary approach for automatically extracting intelligible classification rules

The process of automatically extracting novel, useful and ultimately comprehensible information from large databases, known as data mining, has become of great importance due to the ever-increasing amounts of data collected by large organizations. In particular, the emphasis is devoted to heuristic search methods able to discover patterns that are hard or impossible to detect using standard query mechanisms and classical statistical techniques. In this paper an evolutionary system capable of extracting explicit classification rules is presented. Special interest is dedicated to find easily interpretable rules that may be used to make crucial decisions. A comparison with the findings achieved by other methods on a real problem, the breast cancer diagnosis, is performed.

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