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How Genetic Algorithms Handle Pareto-Optimality in Design and Manufacturing

How Genetic Algorithms Handle Pareto-Optimality in Design and Manufacturing

N. Chakraborti
Copyright: © 2007 |Pages: 18
ISBN13: 9781591409847|ISBN10: 1591409845|EISBN13: 9781591409854
DOI: 10.4018/978-1-59140-984-7.ch031
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MLA

Chakraborti, N. "How Genetic Algorithms Handle Pareto-Optimality in Design and Manufacturing." Handbook of Research on Nature-Inspired Computing for Economics and Management, edited by Jean-Philippe Rennard, IGI Global, 2007, pp. 465-482. https://doi.org/10.4018/978-1-59140-984-7.ch031

APA

Chakraborti, N. (2007). How Genetic Algorithms Handle Pareto-Optimality in Design and Manufacturing. In J. Rennard (Ed.), Handbook of Research on Nature-Inspired Computing for Economics and Management (pp. 465-482). IGI Global. https://doi.org/10.4018/978-1-59140-984-7.ch031

Chicago

Chakraborti, N. "How Genetic Algorithms Handle Pareto-Optimality in Design and Manufacturing." In Handbook of Research on Nature-Inspired Computing for Economics and Management, edited by Jean-Philippe Rennard, 465-482. Hershey, PA: IGI Global, 2007. https://doi.org/10.4018/978-1-59140-984-7.ch031

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Abstract

An informal analysis is provided for the basic concepts associated with multi-objective optimization and the notion of Pareto-optimality, particularly in the context of genetic algorithms. A number of evolutionary algorithms developed for this purpose are also briefly introduced, and finally, a number of paradigm examples are presented from the materials and manufacturing sectors, where multi-objective genetic algorithms have been successfully utilized in the recent past.

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