Current Issue : October - December Volume : 2015 Issue Number : 4 Articles : 4 Articles
This paper presents a meta-objective optimization approach, called Bi-Goal Evolution\n(BiGE), to deal with multi-objective optimization problems with many objectives. In multiobjective\noptimization, it is generally observed that 1) the conflict between the proximity\nand diversity requirements is aggravated with the increase of the number of objectives and\n2) the Pareto dominance loses its effectiveness for a high-dimensional space but works\nwell on a low-dimensional space. Inspired by these two observations, BiGE converts a\ngiven multi-objective optimization problem into a bi-goal (objective) optimization problem\nregarding proximity and diversity, and then handles it using the Pareto dominance relation\nin this bi-goal domain. Implemented with estimation methods of individuals� performance\nand the classic Pareto nondominated sorting procedure, BiGE divides individuals into\ndifferent nondominated layers and attempts to put well-converged and well-distributed\nindividuals into the first few layers. From a series of extensive experiments on four\ngroups of well-defined continuous and combinatorial optimization problems with 5, 10\nand 15 objectives, BiGE has been found to be very competitive against five state-of-the-art\nalgorithms in balancing proximity and diversity. The proposed approach is the first step\ntowards a new way of addressing many-objective problems as well as indicating several\nimportant issues for future development of this type of algorithms....
When students attempt multiple-choice questions (MCQs) they generate invaluable\ninformation which can form the basis for understanding their learning behaviours. In\nthis research, the information is collected and automatically analysed to provide customized,\ndiagnostic feedback to support students� learning. This is achieved within a web-based system,\nincorporating the snap-drift neural network based analysis of students� responses to\nMCQs. This paper presents the results of a large trial of the method and the system which\ndemonstrates the effectiveness of the feedback in guiding students towards a better understanding\nof particular concepts....
Metalearning attracted considerable interest in the machine learning community\nin the last years. Yet, some disagreement remains on what does or what does not constitute\na metalearning problem and in which contexts the term is used in. This survey aims at\ngiving an all-encompassing overview of the research directions pursued under the umbrella\nof metalearning, reconciling different definitions given in scientific literature, listing the\nchoices involved when designing a metalearning system and identifying some of the future\nresearch challenges in this domain....
Human gaze is not directed to the same part of an image when lighting conditions change. Current saliency models do not consider\nlight level analysis during their bottom-up processes. In this paper, we introduce a new saliency model which better mimics\nphysiological and psychological processes of our visual attention in case of free-viewing task (bottom-up process). This model\nanalyzes lighting conditions with the aim of giving different weights to color wavelengths.The resulting saliency measure performs\nbetter than a lot of popular cognitive approaches...
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