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Multi-Objective Evolutionary Algorithms

Multi-Objective Evolutionary Algorithms

Sanjoy Das, Bijaya K. Panigrahi
Copyright: © 2009 |Pages: 7
ISBN13: 9781599048499|ISBN10: 1599048493|EISBN13: 9781599048505
DOI: 10.4018/978-1-59904-849-9.ch167
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MLA

Das, Sanjoy, and Bijaya K. Panigrahi. "Multi-Objective Evolutionary Algorithms." Encyclopedia of Artificial Intelligence, edited by Juan Ramón Rabuñal Dopico, et al., IGI Global, 2009, pp. 1145-1151. https://doi.org/10.4018/978-1-59904-849-9.ch167

APA

Das, S. & Panigrahi, B. K. (2009). Multi-Objective Evolutionary Algorithms. In J. Rabuñal Dopico, J. Dorado, & A. Pazos (Eds.), Encyclopedia of Artificial Intelligence (pp. 1145-1151). IGI Global. https://doi.org/10.4018/978-1-59904-849-9.ch167

Chicago

Das, Sanjoy, and Bijaya K. Panigrahi. "Multi-Objective Evolutionary Algorithms." In Encyclopedia of Artificial Intelligence, edited by Juan Ramón Rabuñal Dopico, Julian Dorado, and Alejandro Pazos, 1145-1151. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-59904-849-9.ch167

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Abstract

Real world optimization problems are often too complex to be solved through analytical means. Evolutionary algorithms, a class of algorithms that borrow paradigms from nature, are particularly well suited to address such problems. These algorithms are stochastic methods of optimization that have become immensely popular recently, because they are derivative-free methods, are not as prone to getting trapped in local minima (as they are population based), and are shown to work well for many complex optimization problems. Although evolutionary algorithms have conventionally focussed on optimizing single objective functions, most practical problems in engineering are inherently multi-objective in nature. Multi-objective evolutionary optimization is a relatively new, and rapidly expanding area of research in evolutionary computation that looks at ways to address these problems. In this chapter, we provide an overview of some of the most significant issues in multi-objective optimization (Deb, 2001).

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