Adaptive intelligence applied to numerical optimisation

The article presents modification strategies theoretical comparison and experimental results achieved by adaptive heuristics applied to numerical optimisation of several non-constraint test functions. The aims of the study are to identify and compare how adaptive search heuristics behave within heterogeneous search space without retuning of the search parameters. The achieved results are summarised and analysed, which could be used for comparison to other methods and further investigation

Label Dependent Evolutionary Feature Weighting for Remote Sensing Data

Nearest neighbour (NN) is a very common classifier used to develop important remote sensing products like land use and land cover (LULC) maps. Evolutive computation has often been used to obtain feature weighting in order to improve the results of the NN. In this paper, a new algorithm based on evolutionary computation which has been called Label Dependent Feature Weighting (LDFW) is proposed. The LDFW method transforms the feature space assigning different weights to every feature depending on each class. This multilevel feature weighting algorithm is tested on remote sensing data from fusion of sensors (LIDAR and orthophotography). The results show an improvement on the NN and resemble the results obtained with a neural network which is the best classifier for the study area

Adaptive elitist-population based genetic algorithm for multimodal function optimization

Abstract. This paper introduces a new technique called adaptive elitistpopulation search method for allowing unimodal function optimization methods to be extended to efficiently locate all optima of multimodal problems. The technique is based on the concept of adaptively adjusting the population size according to the individuals ’ dissimilarity and the novel elitist genetic operators. Incorporation of the technique in any known evolutionary algorithm leads to a multimodal version of the algorithm. As a case study, genetic algorithms(GAs) have been endowed with the multimodal technique, yielding an adaptive elitist-population based genetic algorithm(AEGA). The AEGA has been shown to be very efficient and effective in finding multiple solutions of the benchmark multimodal optimization problems.

Preparation and surface characterization of silica-supported ZrO2 : comparison of layered model systems with powder catalysts

Model supports consisting of a thin layer of SiO2 on a Si single crystal were used to study ZrO2/SiO2/Si model systems made by the same wet chem. prepn. methods as used in the prepn. of tech. catalysts. The results obtained with the models agree with those on ZrO2 catalysts on porous silica supports. [on SciFinder (R)