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

Gupta, Santosh K and Bhat, Shrikant A (2004) Genetic Algorithm and Multi-objective Function Optimization with the Jumping Gene(Transposon) Adaptation-A Primer. In: The excerpts of the lecture clelivered by Prof, Santosli K. Gupta, 14/07/2003, Jamshedpur.



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.

Item Type:Conference or Workshop Item (Lecture)
Uncontrolled Keywords:Genetic Algorithm; Future Endeavours
Divisions:Director Office
ID Code:2972
Deposited By:Sahu A K
Deposited On:06 Jun 2011 14:32
Last Modified:12 Dec 2011 14:40
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