Segmentation is one of the most important tasks in image processing. It consist in classify the pixels into two or more groups depending on their intensity levels and a threshold value. The quality of the segmentation depends on the method applied to select the threshold. The use of the classical implementations for multilevel thresholding is computationally expensive since they exhaustively se...

− A new trend in the design of fuzzy rulebased systems is the use of evolutionary multiobjective optimization (EMO) algorithms. This trend is observed in various areas in machine learning. EMO algorithms are often used to search for a number of Pareto-optimal non-linear systems with respect to their accuracy and complexity. In this paper, we first explain some basic concepts in multiobjective o...

One advantage of evolutionary multiobjective optimization (EMO) algorithms over classical approaches is that many non-dominated solutions can be simultaneously obtained by their single run. In this paper, we propose an idea of using EMO algorithms for constructing an ensemble of fuzzy rule-based classifiers with high diversity. The classification of new patterns is performed based on the vote o...

The aim of this paper is to propose a simple but powerful multiobjective hybrid genetic algorithm and to examine its search ability through computational experiments on commonly used test problems in the literature. We first propose a new multiobjective hybrid genetic algorithm, which is designed by combining local search with an EMO (evolutionary multiobjective optimization) algorithm. In the ...

In this paper, we build mono-lingual and cross-lingual emotion recognition systems and report performance on English and German databases. The emotion recognition system uses biologically inspired auditory attention features together with a neural network for learning the mapping between features and emotion classes. We first build mono-lingual systems for both Berlin Database of Emotional Spee...

Electromagnetism-like Optimization (EMO) is a global optimization algorithm, particularly well-suited to solve problems featuring non-linear and multimodal cost functions. EMO employs searcher agents that emulate a population of charged particles which interact with each other according to electromagnetism’s laws of attraction and repulsion. However, EMO usually requires a large number of itera...

With the advent of efficient techniques for multi-objective evolutionary optimization (EMO), real-world search and optimization problems are being increasingly solved for mulitple conflicting objectives. During the past decade of research and application, most emphasis has been spent on finding the complete Pareto-optimal set, although EMO researchers were always aware of the importance of proc...

For face recognition from video streams speed and accuracy are vital aspects. The first decision whether a preprocessed image region represents a human face or not is often made by a feed-forward neural network (NN), e.g., in the Viisage-FaceFINDER video surveillance system. We describe the optimization of such a NN by a hybrid algorithm combining evolutionary multi-objective optimization (EMO)...

Evolutionary multi objective optimisation EMO now boasts a proliferation of algorithms and bench mark problems We need principled ways to compare the performance of di erent EMO algorithms but this is com plicated by the fact that the result of an EMO run is not a single scalar value but a collection of vectors forming a non dominated set Various metrics for non dominated sets have been suggest...