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...

Based on gathered survey data from Chinese apparel exporters, using structural equation modeling technique, a proposed firm’s export market oriented (EMO) behavior model was empirically tested. A great set of antecedent factors (i.e., organizational structure, export systems, export coordination, top management factors, and export dependence) and moderating factors (i.e., environmental turbulen...

Interactive decision making methods use preference information from the decision maker during the optimization task to guide the search towards favourite solutions. In real-life applications, unforeseen changes in the preferences of the decision maker have to be considered. To the best of our knowledge, no interactive decision making technique has been explicitly designed to recognize and handl...

Sindhya, Karthik Hybrid Evolutionary Multi-Objective Optimization with Enhanced Convergence and Diversity Jyväskylä: University of Jyväskylä, 2011, 64 p.(+included articles) (Jyväskylä Studies in Computing ISSN 1456-5390; 131) ISBN 978-951-39-4372-1 Finnish summary Diss. Evolutionary multi-objective optimization (EMO) algorithms, commonly used to find a set of solutions representing the Pareto ...

Decomposition has become an increasingly popular technique for evolutionary multiobjective optimization (EMO). A decomposition-based EMO algorithm is usually designed to approximate a whole Pareto-optimal front (PF). However, in practice, the decision maker (DM) might only be interested in her/his region of interest (ROI), i.e., a part of the PF. Solutions outside that might be useless or even ...