Evolutionary optimization as applied to inverse scattering problems

Stochastic model reduction applied to inverse problems

This article describes the use of Gaussian Processes in model reduction techniques with application to inverse problems. Mainly, the work is focused on the proper construction of the model approximation, namely on training process based on minimal number of learning samples, by making use of automatic samples selection through computed standard deviation of model prediction. An example of appli...

Scattering : Scattering and Inverse Scattering in Pure and Applied

This series of two volumes brings the concept of scattering in widely different fields – all the way from Nuclear Physics, Atmospheric Sciences to Chemical Physics – into a unified framework. Most of the topics were classified according to the physical scales of the scattering process. This two volume series easily qualifies as the encyclopedia of scattering. There are six parts in this two vol...

Evolutionary Computation Applied to Urban Traffic Optimization

Fuzzy-evolutionary Algorithms Applied to Scheduling Problems

In this work, we will address three sequencing problems: Single Machine, Parallel Machine and Flowshop scheduling. The three problems were studied in previous contributions via approaches based on Memetic Algorithms (MAs). For this work, the original MAs were upgraded with the inclusion of an online running controller based on fuzzy logic for the previously fixed parameters of the evolutionary ...

On Data Mining in Inverse Scattering Problems: Neural Networks Applied to GPR Data Analysis

This paper presents a (semi-)automatic processing technique for GPR data analysis. Exploiting the generalization capabilities of artificial neural networks (ANN), it will be shown that it is possible to feed a Multi-Layer Perceptron (MLP) with a suitable set of input features in order to determine the permittivity of a ground layer. A detailed performance assessment have proven that the algorit...