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 ...

BACKGROUND Questionnaire-based studies suggest atypical sensory perception in over 90% of individuals with autism spectrum conditions (ASC). Sensory questionnaire-based studies in ASC mainly record parental reports of their child's sensory experience; less is known about sensory reactivity in adults with ASC. Given the DSM-5 criteria for ASC now include sensory reactivity, there is a need for a...

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 ...

Multi-objective optimization with more than three objectives has become one of the most active topics in evolutionary multiobjective optimization (EMO). However, most existing studies limit their experiments up to 15 or 20 objectives, although they claimed to be capable of handling as many objectives as possible. To broaden the insights in the behavior of EMO methods when facing a massively lar...

Evolutionary multiobjective optimization (EMO) optimizes multi-objectives, conflicting with each other, simultaneously. In this paper, EMO has been utilized to improve a window based protocol based on two parameters, Bandwidth Delay Product (BDP) and end-to-end delay. The problem has been simulated using gamultiobj tool in the MATLAB.

This paper discusses the implementation of local search in evolutionary multiobjective optimization (EMO) algorithms for the design of a simple but powerful memetic EMO algorithm. First we propose a basic framework of our memetic EMO algorithm, which is a hybrid algorithm of the NSGA-II and local search. In the generation update procedure of our memetic EMO algorithm, the next population is con...

The Early Music Online (EMO) collection consists of about 300 printed music books of the sixteenth century held at the British Library. They were recently digitized from microfilms and made available online. In total, about 35,000 pages were digitized. This paper presents an optical music recognition (OMR) evaluation on the EMO collection. Firstly, the content of the collection is reviewed, loo...