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Genetic Algorithm Based Feature Ranking in Multi-criteria Optimization


N.Suguna, K.Thanushkodi


Vol. 9  No. 6  pp. 132-141


Evolutionary algorithms such as Genetic Algorithms (GAs) have become the method of choice for optimization problems that are too complex due to their advantages compared to other methods. GAs require little knowledge about the problem being solved, and they are easy to implement, robust, and inherently parallel. GAs often take less time to find the optimal solution than other methods. However, most real-world problems involve simultaneous optimization of several often mutually concurrent objectives. GAs are able to find optimal solutions in an overall sense. This paper deals with a special case of multi-objective optimization problems from the medical domain which are of a very high practical relevance. One of the problems is to rank the treatments for Trigeminal Neuralgia. The second problem is to rank the risk factors for Bronchial Asthma. We use a simple multiple objective procedure and an evolutionary scheme for solving the problems. Results obtained by the proposed approach in a very simple way are same as the results (or even better) obtained by applying weighted-sum method. The advantage of the proposed technique is that it does not require any additional information about the problem.


Multi-Criteria Optimization, Feature Ranking, Genetic Algorithm, Trigeminal Neuralgia, Bronchial Asthma