Hybrid sampling on mutual information entropy-based clustering ensembles for optimizations

In this paper, we focus on the design of bivariate EDAs for discrete optimization problems and propose a new approach named HSMIEC. While the current EDAs require much time in the statistical learning process as the relationships among the variables are too complicated, we employ the Selfish gene theory (SG) in this approach, as well as a Mutual Information and Entropy based Cluster (MIEC) model is also set to optimize the probability distribution of the virtual population. This model uses a hybrid sampling method by considering both the clustering accuracy and clustering diversity and an incremental learning and resample scheme is also set to optimize the parameters of the correlations of the variables. Compared with several benchmark problems, our experimental results demonstrate that HSMIEC often performs better than some other EDAs, such as BMDA, COMIT, MIMIC and ECGA. © 2009 Elsevier B.V. All rights reserved.

Business information systems:technology, development and management for the e-business

In order to run a successful business, today’s manager needs to combine business skills with an understanding of information systems and the opportunities and benefits that they bring to an organisation. Starting from basic concepts, this book provides a comprehensive and accessible guide to: •understanding the technology of business information systems; •choosing the right information system for an organisation; •developing and managing an efficient business information system; •employing information systems strategically to achieve organisational goals. Taking a problem-solving approach, Business Information Systems looks at information systems theory within the context of the most recent business and technological advances. This thoroughly revised new edition has updated and expanded coverage of contemporary key topics such as: •Web 2.0 •enterprise systems •implementation and design of IS strategy •outsourcing