A robust evolutionary framework for multi-objective optimization

Deb, Kalyanmoy (2008) A robust evolutionary framework for multi-objective optimization Proceeding of Genetic and Evolutionary Computation conference (GECCO-2008), (Atlanta, USA) . pp. 633-640.

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Official URL: http://dl.acm.org/citation.cfm?id=1389223

Related URL: http://dx.doi.org/10.1145/1389095.1389223

Abstract

Evolutionary multi-objective optimization (EMO) methodologies, suggested in the beginning of Nineties, focussed on the task of finding a set of well-converged and well-distributed set of solutions using evolutionary optimization principles. Of the EMO methodologies, the elitist non-dominated sorting genetic algorithm or NSGA-II, suggested in 2000, is now probably the most popularly used EMO procedure. NSGA-II follows three independent principles-domination principle, diversity preservation principle and elite preserving principle-which make NSGA-II a flexible and robust EMO procedure in the sense of solving various multi-objective optimization problems using a common framework. In this paper, we describe NSGA-II through a functional decomposition following the implementation of these three principles and demonstrate how various multi-objective optimization tasks can be achieved by simply modifying one of the three principles. We argue that such a functionally decomposed and modular implementation of NSGA-II is probably the reason for it's popularity and robustness in solving various types of multi-objective optimization problems.

Item Type:Article
Source:Copyright of this article belongs to Proceeding of Genetic and Evolutionary Computation conference (GECCO-2008), (Atlanta, USA).
ID Code:81036
Deposited On:03 Feb 2012 11:44
Last Modified:03 Feb 2012 11:44

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