Genetic Algorithm and Multi-objective Function Optimization with the Jumping Gene(Transposon) Adaptation-A Primer

I am going to deliver a lecture on "Genetic Algorithm and Multi-objective Optimization with the Jumping Gene (Transposon ) Adaptation - A Primer".Optimization techn-iques have long been applied to problems of industrial importance.Several excellent texts1 -5 describe the vari-ous methods with examples. These usually involve a single objective function and constraints . Most real-world
engineering problems, however, require the simultaneous optimization of several objectives ( multi-objective optimization ) that cannot be compared easily with each
other (are non-commensurate), and so cannot be combined into a single , meaningful scalar objective function. An example is the maximization of the product, while mini-mizing the production of an undesirable side product. A very popular and robust technique for solving optimiza-tion problems with a single objective function is genetic algorithm (GA), also referred to as simple GA (SGA). This, is a search technique developed by Holland .It mimics the process of natural selection and natural genetics. The Darwinian principle of'survival of the fittest ' is used to obtain the optimal solution. This technique is better than calculus-based methods (both direct and indirect methods)that generally obtain the local optimum, and that may miss the global optimum . This technique does not need derivatives either. A recent adaptation of GA [non domi-nated sorting genetic algorithm' with elitism ' and the
jumping gene operator, NSGA II-JG4] has been developed to solve multi-objective function optimization problems. In this paper we describe GA and its adaptations in a manner quite suited to a beginner.