Multi-objective Evolutionary Algorithms for Classification: A Review

Seema Mane, Prof. S. S. Sonawani, Dr. Sachin Sakhare and Prof. P. V. Kulkarni

ABSTRACT Multi-objective evolutionary algorithms are evolutionary systems which are used for optimizing various measures of the evolving systems. Most of the real life data mining problems are optimization problems, where the aim is to evolve a candidate model that optimizes certain performance criteria. Classification problem can be thought of as multi-objective problem as it may require to optimize accuracy, model complexity, interestingness, misclassification rate, sensitivity, specificity etc. The performance of these MOEAs used is depends on various characteristics like evolutionary techniques used, chromosome representation, parameters like population size, crossover rate, mutation rate, stopping criteria, number of generations, objectives taken for optimization, fitness function used, optimization strategy etc. This paper reports the comprehensive survey on recent developments in the multi-objective evolutionary algorithms for classification problems. Keywords:- Multi-objective Optimization, Evolutionary algorithms, Classification, Pareto optimality, Genetic Algorithm.

Title:	Multi-objective Evolutionary Algorithms for Classification: A Review
Author Name:	Seema Mane, Prof. S. S. Sonawani, Dr. Sachin Sakhare and Prof. P. V. Kulkarni
Abstract:	ABSTRACT Multi-objective evolutionary algorithms are evolutionary systems which are used for optimizing various measures of the evolving systems. Most of the real life data mining problems are optimization problems, where the aim is to evolve a candidate model that optimizes certain performance criteria. Classification problem can be thought of as multi-objective problem as it may require to optimize accuracy, model complexity, interestingness, misclassification rate, sensitivity, specificity etc. The performance of these MOEAs used is depends on various characteristics like evolutionary techniques used, chromosome representation, parameters like population size, crossover rate, mutation rate, stopping criteria, number of generations, objectives taken for optimization, fitness function used, optimization strategy etc. This paper reports the comprehensive survey on recent developments in the multi-objective evolutionary algorithms for classification problems. Keywords:- Multi-objective Optimization, Evolutionary algorithms, Classification, Pareto optimality, Genetic Algorithm.
Cite this article:	Seema Mane, Prof. S. S. Sonawani, Dr. Sachin Sakhare and Prof. P. V. Kulkarni , " Multi-objective Evolutionary Algorithms for Classification: A Review " , International Journal of Application or Innovation in Engineering & Management (IJAIEM), Volume 3, Issue 10, October 2014 , pp. 292-297 , ISSN 2319 - 4847.
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