This paper puts forward a comprehensive study of the design of global stopping criteria for multi-objective optimization. In this study we propose a global stopping criterion, which is terms as MGBM after the authors surnames. MGBM combines a novel progress indicator, called mutual domination rate (MDR) indicator, with a simplified Kalman filter, which is used for evidence-gathering purposes. T...

The hypervolume indicator is widely used to guide the search and to evaluate the performance of evolutionary multi-objective optimization algorithms. It measures the volume of the dominated portion of the objective space which is considered to give a good approximation of the Pareto front. There is surprisingly little theoretically known about the quality of this approximation. We examine the m...

Indicator-based evolutionary algorithms are amongst the best performing methods for solving multi-objective optimization (MOO) problems. In reinforcement learning (RL), introducing a quality indicator in an algorithm’s decision logic was not attempted before. In this paper, we propose a novel on-line multi-objective reinforcement learning (MORL) algorithm that uses the hypervolume indicator as ...

The aims of this paper are to determine the reliability and validity of Entrepreneurial Attitude Orientation (EAO) scale as an instrument to measure the entrepreneurial attitudes among business students of higher education institutes of Malaysia. Initially, the EAO scale consisted of 75 items concerning 4 conceptual subscales: achievement in business (23 items), perceived personal control of bu...

Ecological indicators, historical land use, and invasive species detection in the lower Iowa River floodplain." MA has been approved by the Examining Committee for the thesis requirement for the Master of Arts degree in Geography at the May 2014 graduation.

We address the question how citation-based bibliometric indicators can best be normalized to ensure fair comparisons between publications from different scientific fields and different years. In a systematic large-scale empirical analysis, we compare a traditional normalization approach based on a field classification system with three source normalization approaches. We pay special attention t...

Concerned with multi-objective reinforcement learning (MORL), this paper presents MOMCTS, an extension of Monte-Carlo Tree Search to multi-objective sequential decision making. The known multi-objective indicator referred to as hyper-volume indicator is used to define an action selection criterion, replacing the UCB criterion in order to deal with multi-dimensional rewards. MO-MCTS is firstly c...

The poverty mapping methodology for estimating welfare rankings from small areas has proven to be useful in guiding allocation of government funds, regional planning, and general policy formulation. Nevertheless, poverty mapping also suffers from a series of by now well recognized shortcomings. We apply an approach based on first order dominance (FOD) to small area estimation. Five advantages t...