Fundamentals of Genetic Algorithm and Applications
Genetic Algorithms (GAs) are randomized search and optimization techniques guided by the principles of evolution and natural genetics. They have been extensively used in solving hard optimization problems in a variety of real-life domains. The essential components of GAs are a strategy for representing or encoding a solution to the problem under consideration, a criterion for evaluating the fitness or goodness of an encoded solution and a set of biologically inspired operations, selection, crossover and mutation, applied on the encoded solutions. The conventional GAs are designed to optimize just a single criterion. However, many real-life problems have multiple conflicting objectives that need to be simultaneously optimized. Such problems are referred to as multiobjective optimization (MOO) problems. In contrast to single objective optimization, which yields a single best solution, in MOO the final solution set contains a number of Pareto-optimal solutions, none of which can be further improved on any one objective without degrading it in another. Since GAs typically adopt a population based search, modeling GAs to solve MOO problems is natural. The present talk will first explain the basic principles of genetic algorithms. This will be followed by a description of how GAs can be applied to clustering into a fixed number of partitions. Its extension to the case of unknown number of clusters will be discussed. The problem of multiobjective optimization will then be discussed in detail, and a GA based approach will be discussed. Finally, an application of genetic algorithm for clustering a data set will be described. Biosketch: Sanghamitra Bandyopadhyay joined the Machine Intelligence Unit of the Indian Statistical Institute as a faculty member in 1999, after completing her PhD from the same Institute.. She is currently the Director of the Institute. Her areas of research interest include computational biology and bioinformatics, soft and evolutionary computation, pattern recognition and data mining. In these areas she has published more than 300 research articles in various journal, conferences and edited volumes. She has published six authored and edited books from publishers like Springer, World Scientific and Wiley. Sanghamitra has worked in various Universities and Institutes world-wide including in USA, Australia, Germany, France, Italy, China, Slovenia and Mexico, and delivered invited lectures in many more countries. She has received several awards and fellowships including the Bhatnagar Prize, Infosys award, TWAS Prize, DBT National Women Bioscientist Award (Young), INAE Silver Jubilee Prize, Young scientist/engineer medals of INSA, INAE and Science Congress, JC Bose Fellowship, Swarnajayanti Fellowship and Humboldt Fellowship. She is a Senior Associate of ICTP and Fellow of INSA, INAE, NASI and IEEE. She is currently a member of the Science, Technology and Innovation Advisory Council of the Prime Minister of India.
