P. N. Suganthan, Nanyang Technological University, Singapore

Associate Professors Ponnuthurai Nagaratnam Suganthan received the B.A degree, Postgraduate Certificate and M.A degree in Electrical and Information Engineering from the University of Cambridge, UK in 1990, 1992 and 1994, respectively. He obtained his Ph.D. degree from the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore. He was a predoctoral Research Assistant in the Department of Electrical Engineering, University of Sydney in 1995–96 and a lecturer in the Department of Computer Science and Electrical Engineering, University of Queensland in 1996–99. Since 1999 he has been with the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore where he was an Assistant Professor and now is an Associate Professor. He is an associate editor of the IEEE Trans on Evolutionary Computation, Information Sciences, Pattern Recognition and Int. J. of Swarm Intelligence Research Journals. He is a founding co-editor-in-chief of Swarm and Evolutionary Computation, an Elsevier journal. SaDE (April 2009) paper won "IEEE Transactions on Evolutionary Computation" outstanding paper award. His research interests include evolutionary computation, pattern recognition, multi-objective evolutionary algorithms, bioinformatics, applications of evolutionary computation and neural networks. He is a Senior Member of the IEEE. He has delivered around 10 plenary talks, tutorials and invited talks at international meetings in North America, Europe and Asia. Further details are available from his home page: http://www.ntu.edu.sg/home/epnsugan/

Abstract

Learning Approaches for Search and Optimization Algorithms
Search and optimization algorithms have been employed in all disciplines to solve complex problems. Evolutionary and heuristic search and optimization algorithms are more commonly used recently as modern search and optimization problems may not satisfy the requirements of traditional optimization algorithms. Traditionally, these search algorithms require users to make decisions regarding the choices of operators, parameter values, etc. by trial and error before applying these algorithms. In recent years, learning algorithms are being integrated with search and optimization algorithms to enhance the performances of these search and optimization algorithms. This tutorial proposes to present the hybridization between learning algorithms and search & optimization algorithms.