Depression and suicide present a serious health problem especially for teenagers as they are increasingly diagnosed with mood disorders of different severity, possibly leading to suicidal activity. Reported here is a misfortunate young girl who committed suicide by jumping from high altitude. She left a suicide note which, together with her behavior in the death-preceding period, pointed to her...

Multiobjective optimization deals with finding and evaluating a number of trade-off optimal solutions. Evolutionary multiobjective optimization (EMO), started in early nineties, is now a fast-growing field of research and application in evolutionary computation. Numerous different algorithms have been developed to address computationally complex problems. Many of these algorithms attempt to fin...

The QoS-driven Service Selection (QSS) problem is a wellknown NP-hard problem in the combinatorial optimization field. Although the QSS problem is naturally multi-objective optimization problem, most of the existing approaches solve the problem in single-objective optimization context. In the recent years, there have been some efforts to tackle the problem in multi-objective optimization contex...

Order acceptance and scheduling (OAS) is an important issue in make-to-order production systems that decides the set of orders to accept and the sequence in which these accepted orders are processed to increase total revenue and improve customer satisfaction. This paper aims to explore the Pareto fronts of trade-off solutions for a multi-objective OAS problem. Due to its complexity, solving thi...

BACKGROUND EMO syndrome, defined as a triad including exophthalmus, pretibial myxedema and osteoarthropathia, is a rare condition in patients suffering from hyperthyreosis. CASE PRESENTATION We here describe an interesting case of EMO syndrome associated with unilateral fibromatosis of the hand and an initial stage of generalized myxedema of the skin. To our knowledge a similar case has not y...

Themain advantage of evolutionary multi-objective optimization (EMO) over classical approaches is that a variety of non-dominated solutions with a wide range of objective values can be simultaneously obtained by a single run of an EMO algorithm. In this chapter, we show how this advantage can be utilized in the design of fuzzy ensemble classifiers. First we explain three objectives in multi-obj...

The objective functions in multiobjective optimization problems are often non-linear, noisy, or not available in a closed form and evolutionary multiobjective optimization (EMO) algorithms have been shown to be well applicable in this case. Here, our objective is to facilitate interactive decision making by saving function evaluations outside the “interesting” regions of the search space within...

In many real-world applications concerning multi-objective optimization, the true objective functions are not observable. Instead, only noisy observations are available. In recent years, the interest in the effect of such noise in evolutionary multi-objective optimization (EMO) has increased and many specialized algorithms have been proposed. However, evolutionary algorithms are not suitable if...

A main focus of current research on evolutionary multiobjective optimization (EMO) is the study of the effectiveness of EMO algorithms for problems with many objectives. Among the several techniques that have led to the development of more effective algorithms, decomposition and component-wise design have presented particularly good results. But how do they compare? In this work, we conduct a s...

During business collaboration, partners may benefit through sharing data. People may use data mining tools to discover useful relationships from shared data. However, some relationships are sensitive to the data owners and they hope to conceal them before sharing. In this paper, we address this problem in forms of association rule hiding. A hiding method based on evolutionary multi-objective op...