CS Boardroom ICCS X836
Problems involving multiple conflicting objectives arise in most real world optimization problems. Evolutionary Algorithms (EAs) have gained a wide interest and success in solving problems of this nature for two main reasons: (1) EAs allow finding several members of the Pareto optimal set in a single run of the algorithm and (2) EAs are less susceptible to the shape of the Pareto front. Thus, Multi-objective EAs (MOEAs) have often been used to solve Multi-objective Problems (MOPs). This talk aims to summarize the efforts of various researcher’s algorithmic processes for MOEAs in an attempt to provide a review of the use and the evolution of the field. Hence, some basic concepts and a summary of the main MOEAs are provided. Furthermore, few interesting engineering applications will be discussed.
Dr. Rituparna Datta is working as Computer Research Scientist in the University of South Alabama, USA. Prior to that, he was a Operations Research Scientist in Boeing Research & Technology (BR&T), Boeing, India. His current research work involves investigation of efficient algorithm for engineering optimization, evolutionary computation, machine learning, constraint handling, memetic algorithms, derivative-free optimization, knowledge extraction from data, manufacturing and robotics. His research has been published in 10 international SCI journals, few book chapters and 20+ international conferences with two edited books with Springer (one of them is the first book in the Infosys Science Foundation Series).