A. Iazzetta

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.

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.

Optimizing Neural Networks for Time Series Prediction

In this paper we investigate the effective design of an appropriate neural network model for time series prediction based on an evolutionary approach. In particular, the Breeder Genetic Algorithms are considered to face contemporaneously the optimization of (i) the design of a neural network architecture and (ii) the choice of the best learning method. The effectiveness of the approach proposed is evaluated on a standard benchmark for prediction models, the Mackey-Glass series.

Evolutionary Algorithms for Aerofoil Design

Abstract This paper wishes lo describe Evolutionary Algorithms as an effective means for the solution of the Aerofoil Design Optimisation in Aerodynamics. Firstly the basic ideas underlying Evolutionary Algorithms arc outlined. Several versions of Evolutionary Algorithms arc briefly described, focussing on their similarities and on their differences as well. Then their application to both Direct and Inverse Aerofoil Design Problem is described, and results arc given. Finally, several possible parallel models for Evolutionary Algorithms are discussed, and the results of the application of one of them to the above problem arc presented.

MijnMutation Operator for Aerofoil Design Optimisation

A new mutation operator, called M ijn , capable of operating on a set of adjacent bits in one single step, is introduced. Its features are examined and compared against those of the classical bit-flip mutation. A simple Evolutionary Algorithm, M-EA, is described which is based only on selection and M ijn This algorithm is used for the solution of an industrial problem, the Inverse Aerofoil Design optimisation, characterised by high search time to achieve satisfying solutions, and its performance is compared against that offered by a classical binary Genetic Algorithm. The experiments show for our algorithm a noticeable reduction in the time needed to reach a solution of acceptable quality, thus they prove the effectiveness of the proposed operator and its superiority to GAs for the problem at hand.

A new mutation operator for evolutionary airfoil design

Abstract A new mutation operator, ℳijn, capable of operating on a set of adjacent bits in one single step, is introduced. Its features are examined and compared against those of the classical bit–flip mutation. A simple Evolutionary Algorithm, ℳ–EA, based only on selection and ℳijn, is described. This algorithm is used for the solution of an industrial problem, the Inverse Airfoil Design optimization, characterized by high search time to achieve satisfying solutions, and its performance is compared against that offered by a classical binary Genetic Algorithm. The experiments show for our algorithm a noticeable reduction in the time needed to reach a solution of acceptable quality, thus they prove the effectiveness of the proposed operator and its superiority to GAs for the problem at hand.