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In this study, we present the Big Bang-Big Crunch (BB-BC) method to solve the post-enrolment course timetabling problem. This method is derived from one of the evolution of the universe theories in physics and astronomy. The BB-BC theory involves two phases (Big Bang and Big Crunch). The Big Bang phase feeds the Big Crunch phase with many inputs and the Big Crunch phase is the shrinking destiny of...
It is well known that the problem arising from high dimensionality of data should be considered in pattern recognition field. Face recognition databases are usually high dimensionality, especially when limited training samples are available for each subject. Traditional techniques perform dimensionality reduction are unable to solve this problem smoothly, which makes feature extraction task much difficult...
Non-negative matrix factorization is an important method helpful in the analysis of high dimensional datasets. It has a number of applications including pattern recognition, data clustering, information retrieval or computer security. One its significant drawback lies in its computational complexity. In this paper, we introduce a new method allowing fast approximate transformation from input space...
Attribute reduction is one of the main issues in the theoretical research of rough set theory which is known as a NP-hard optimization problem. The objective is to find the minimal number of attributes from a large dataset. Hence it is difficult to solve to optimality. This paper proposes a composite neighbourhood structure approach to solve the attribute reduction problem that consists of two versions...
The generalized assignment problem is a well-known NP-complete problem whose objective is to find a minimum cost assignment of a set of jobs to a set of agents by considering the resource constraints. Dynamic instances of the generalized assignment problem can be created by changing the resource consumptions, capacity constraints and costs of jobs. Memory-based approaches are among a set of evolutionary...
This paper presents a methodology to find optimal solutions for linear programming problems on imprecise conditions. By using α-cuts, the cumulative membership function and the classic fuzzy linear programming model, a fuzzy joint parameters where its left hand side is defined by any kind of fuzzy set and its right hand side is defined by linear fuzzy sets, is solved and its crisp output is found...
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