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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...
The advantages of soft c-means over its hard and fuzzy versions render it more attractive to use in a wide variety of applications. Its main merit lies in its relatively higher convergence speed, which is more obvious in the presence of huge high dimensional data. This work presents a new approach to accelerate the convergence of the original soft c-means. It is mainly based on an iterative optimization...
This paper deals with the problem of multi-agent learning of a population of players, engaged in a repeated normal-form game. Assuming boundedly-rational agents, we propose a model of social learning based on trial and error, called “social reinforcement learning”. This extension of well-known Q-learning algorithm, allows players within a population to communicate and share their experiences with...
Studies on content-based music retrieval (CBMR) which search music by analyzing their acoustic features and defining their similarity, have been conducted actively. However, it is desirable that the similarity evaluation be adaptive to each user's demand, because the search criteria differs user by user. In this paper, we propose a framework of CBMR that tries to satisfy the various demands of different...
Genetic algorithm and particle swarm optimization are two methods which can be used to find the global extremum of cost functions. The solely performance of each method and their specific characteristics in finding the global extremum have been giving the idea of hybridization of these two methods to many researchers. In this paper a new hybrid algorithm named Serial Genetic Algorithm and Particle...
We propose a classification model for the cognitive level of question items in examinations based on Bloom's taxonomy. The model implements the artificial neural network approach, which is trained using the scaled conjugate gradient learning algorithm. Several data preprocessing techniques such as word extraction, stop word removal, stemming, and vector representation are applied to a feature set...
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