We investigate the potential of a microgenetic algorithm (MGA) as a generalized hill-climbing operator. Combining a standard GA with the suggested MGA operator leads to a hybrid genetic scheme GA-MGA, with enhanced searching qualities. The main GA performs global search while the MGA explores a neighborhood of the current solution provided by the main GA, looking for better solutions. The MGA operator performs genetic local search. The major advantage of MGA is its ability to identify and follow narrow ridges of arbitrary direction leading to the global optimum. The proposed GA-MGA scheme is tested against 13 different schemes, including a simple GA and GAs with different hill-climbing operators. Experiments are conducted on a test set including eight constrained optimization problems with continuous variables. Extensive simulation results demonstrate the efficiency of the proposed GA-MGA scheme. For the same number of fitness evaluations, GA-MGA exhibited a significantly better performance in terms of solution accuracy, feasibility percentage of the attained solutions, and robustness.

This article presents a comparative study between scale, rotation and translation invariant descriptors for shape representation and retrieval. Since shape is one of the most widely used image feature exploited in content-based image retrieval systems, the authors studied for each descriptor, the number of coefficients needed for indexing and their retrieval performance. Specifically, the authors studied Fourier, curvature scale space, angular radial transform (ART) and image moment descriptors for shape representation. The four shape descriptors are evaluated against each other using the standard methodology and the two most appropriate and available databases. The results showed that moment descriptors present the best performance in terms of shape representation quality while ART presents the lowest descriptor size.

This paper proposes a fundamentally novel extension, namely, flrFAM, of the fuzzy ARTMAP (FAM) neural classifier for incremental real-time learning and generalization based on fuzzy lattice reasoning techniques. FAM is enhanced first by a parameter optimization training (sub)phase, and then by a capacity to process partially ordered (non)numeric data including information granules. The interest here focuses on intervals' numbers (INs) data, where an IN represents a distribution of data samples. We describe the proposed flrFAM classifier as a fuzzy neural network that can induce descriptive as well as flexible (i.e., tunable) decision-making knowledge (rules) from the data. We demonstrate the capacity of the flrFAM classifier for human facial expression recognition on benchmark datasets. The novel feature extraction as well as knowledge-representation is based on orthogonal moments. The reported experimental results compare well with the results by alternative classifiers from the literature. The far-reaching potential of fuzzy lattice reasoning in human-machine interaction applications is discussed.