Optimization is an activity which does not belong to any particular discipline and is routinely used in almost all fields of science, engineering and commerce. The Chambers dictionary describes optimization as an act of ‘making the most or best of anything'. Theoretically speaking, performing an optimization task in a problem means finding the most or best suitable solution of the problem. Mathematical optimization studies spend a great deal of effort in trying to describe the properties of such an ideal solution. Engineering or practical optimization studies, on the other hand, thrive to look for a solution which is as similar to such an ideal solution as possible. Although the ideal optimal solution is desired, the restrictions on computing power and time often make the practitioners happy with an approximate solution. Serious studies on practical optimization begun as early as the Second World War, when the need for efficient deployment and resource allocation of military personnel and accessories became important. Most development in the so-called ‘classical' optimization field was made by developing step-by-step procedures for solving a particular type of an optimization problem. Often fundamental ideas from geometry and calculus were borrowed to reach the optimum in an iterative manner. Such optimization procedures have enjoyed a good 50 years of research and applications and are still going strong. However, around the middle of eighties, completely unorthodox and less-mathematical yet intriguing optimization procedures have been suggested mostly by computer scientists. It is not surprising because these ‘non-traditional' optimization methods exploit the fast and distributed computing machines which are getting increasingly available and affordable like slide-rules of sixties.
Prof. Deb's research is focused on one such non-traditional optimization method which takes the lion's share of all non-traditional optimization methods. This so-called ‘evolutionary algorithm (EA)' mimics the natural evolutionary principles on randomly-picked solutions from the search space of the problem and iteratively progresses towards the optimum point. Nature's ruthless selective advantage to fittest individuals and creation of new and fit individuals using recombinative and mutative genetic processing with generations is well-mimicked artificially in a computer algorithm to be played on a search space where good and bad solutions to the underlying problem coexist. The task of an evolutionary optimization algorithm is then to avoid the bad solutions in the search space, take clues from good solutions and eventually reach close to the best solution, similar to the genetic processing in natural systems.
In the early nineties, genetic algorithms were known only to a few computer scientists and applicationists across the world. Since his joining IIT Kanpur in 1992, Prof. Deb has systematically perfected the methodology so it could be used in various engineering problem solving tasks efficiently. His major contribution in this field has been the development of different genetic algorithms, of which the most significant one had been the multi-objective genetic algorithm (which he called NSGA-II), which allows to achieve a design in the presence of conflicting objectives, such as simultaneously minimizing the cost of production and maximizing the life of the product. This idea, put forth in 1994 from his Kanpur Genetic Algorithms Laboratory (KanGAL), has now been developed into a field of its own having major commercial softwares, dedicated international conferences, books and over 120 PhD theses across the world. Some of his other contributions which are also popularly used and are adopted in practice are (i) a parameter-less constraint-handling procedure for handling non-linear constraints, (ii) real-parameter genetic algorithms for handling mixed real and discrete variables often arise in engineering problem solving, (iii) large-scale genetic algorithms capable of solving around a million of variables, (iv) multi-modal GAs, (v) multi-objective GAs for handling uncertainties in problem and decision variables, (vi) reliability-based design optimization using genetic algorithms, and (vii) machine learning problem solving. In a nut-shell, Prof. Deb's research at IIT Kanpur has made the use of genetic algorithms in single and multi-objective optimization plausible and popular in engineering problem solving. Besides developing fundamental methodologies for optimal design, Prof. Deb has also been actively involved in applying them to industries in India and abroad, including aerospace, automobile, electronic, electrical and IT-based industries.