Multi-objective optimization using genetic algorithms: A tutorial

by: Abdullah Konak, David W Coit, Alice E Smith

Reliability Engineering & System Safety, Vol. 91, No. 9. (September 2006), pp. 992-1007.

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Abstract

Multi-objective formulations are realistic models for many complex engineering optimization problems. In many real-life problems, objectives under consideration conflict with each other, and optimizing a particular solution with respect to a single objective can result in unacceptable results with respect to the other objectives. A reasonable solution to a multi-objective problem is to investigate a set of solutions, each of which satisfies the objectives at an acceptable level without being dominated by any other solution. In this paper, an overview and tutorial is presented describing genetic algorithms (GA) developed specifically for problems with multiple objectives. They differ primarily from traditional GA by using specialized fitness functions and introducing methods to promote solution diversity.

@article{citeulike:1300532, abstract = {Multi-objective formulations are realistic models for many complex engineering optimization problems. In many real-life problems, objectives under consideration conflict with each other, and optimizing a particular solution with respect to a single objective can result in unacceptable results with respect to the other objectives. A reasonable solution to a multi-objective problem is to investigate a set of solutions, each of which satisfies the objectives at an acceptable level without being dominated by any other solution. In this paper, an overview and tutorial is presented describing genetic algorithms (GA) developed specifically for problems with multiple objectives. They differ primarily from traditional GA by using specialized fitness functions and introducing methods to promote solution diversity.}, author = {Konak, Abdullah and Coit, David W. and Smith, Alice E. }, booktitle = {Special Issue - Genetic Algorithms and Reliability}, citeulike-article-id = {1300532}, journal = {Reliability Engineering \& System Safety}, keywords = {evolutionary}, month = {September}, number = {9}, pages = {992--1007}, posted-at = {2008-04-30 14:54:17}, priority = {2}, title = {Multi-objective optimization using genetic algorithms: A tutorial}, url = {http://www.sciencedirect.com/science/article/B6V4T-4J0NY2F-2/2/97db869c46fc43f457f3d509adaa15b5}, volume = {91}, year = {2006} }

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TY - JOUR ID - citeulike:1300532 L3 - citeulike-article-id:1300532 TI - Multi-objective optimization using genetic algorithms: A tutorial T2 - Special Issue - Genetic Algorithms and Reliability JF - Reliability Engineering & System Safety VL - 91 IS - 9 SP - 992 EP - 1007 N2 - Multi-objective formulations are realistic models for many complex engineering optimization problems. In many real-life problems, objectives under consideration conflict with each other, and optimizing a particular solution with respect to a single objective can result in unacceptable results with respect to the other objectives. A reasonable solution to a multi-objective problem is to investigate a set of solutions, each of which satisfies the objectives at an acceptable level without being dominated by any other solution. In this paper, an overview and tutorial is presented describing genetic algorithms (GA) developed specifically for problems with multiple objectives. They differ primarily from traditional GA by using specialized fitness functions and introducing methods to promote solution diversity. KW - evolutionary AU - Konak, Abdullah AU - Coit, David AU - Smith, Alice PY - 2006/09// UR - http://www.sciencedirect.com/science/article/B6V4T-4J0NY2F-2/2/97db869c46fc43f457f3d509adaa15b5 ER -

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