Description
With the large amount of data stored by many organizations, capitalists have observed that this information is an intangible asset. Unfortunately, handling large databases is a very complex process and traditional learning techniques are expensive to use. Heuristic techniques provide much help in this arena, although little is known about heuristic techniques. Heuristic and Optimization for Knowledge Discovery addresses the foundation of this topic, as well as its practical uses, and aims to fill in the gap that exists in current literature.
Author's/Editor's Biography
Hussein Abbass (Ed.)
Hussein A. Abbass is the director of the Artificial Life and Adaptive Robotics Laboratory at the School of Information Technology and Electrical Engineering at the Australian Defense Force Academy campus of the University of New South Wales. Dr. Abbass is a Senior Member of the IEEE and has more than 15 years experience in industry and academia and more than a hundred fully refereed papers in international journals and conferences. He teaches computational intelligence related subjects and his research focuses on multi-agent systems, data mining, and artificial life models with applications to defence, security and business.
Charles Newton (Ed.)
Charles S. Newton is the Head of Computer Science, University of New South Wales (UNSW) at the Australian Defence Force Academy (ADFA) campus, Canberra. Prof. Newton is also the Deputy Rector (Education). He obtained his Ph.D. in Nuclear Physics from the Australian National University, Canberra in 1975. He joined the School of Computer Science in 1987 as a Senior Lecturer in Operations Research. In May 1993, he was appointed Head of School and became Professor of Computer Science in November 1993. Prior to joining at ADFA, Prof. Newton spent nine years in the Analytical Studies Branch of the Department of Defence. In 1989-91, Prof. Newton was the National President of the Australian Society for Operations Research. His Research Interests encompass Group Decision Support Systems, Simulation, Wargaming, Evolutionary Computation, Data Mining and Operations Research Applications. He has published extensively in national and international journals, books and conference proceedings.
Ruhul Sarker (Ed.)
Ruhul Sarker received his Ph.D. in 1991 from DalTech, Dalhousie University, Halifax, Canada, and is currently a Senior Lecturer in Operations Research at the School of Computer Science, University of New South Wales, ADFA Campus, Canberra, Australia. Before joining at UNSW in February 1998, Dr Sarker worked with Monash University, Victoria, and the Bangladesh University of Engineering and Technology, Dhaka. His main research interests are Evolutionary Optimization, Data Mining and Applied Operations Research. He is currently involved with four edited books either as editor or co-editor, and has published more than 60 refereed papers in international journals and conference proceedings. He is also the editor of ASOR Bulletin, the national publication of the Australian Society for Operations Research.