Generalized Extremal Optimization 41 Copyright © 2005, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. Chapter III Generalized Extremal Optimization: A New Meta-Heuristic Inspired by a Model of Natural Evolution Fabiano Luis de Sousa, INPE, Brazil Fernando Manuel Ramos, INPE, Brazil Roberto Luiz Galski, INPE, Brazil Issamu Muraoka, INPE, Brazil ABSTRACT In this chapter a recently proposed meta-heuristic devised to be used in complex optimization problems is presented. Called Generalized Extremal Optimization (GEO), it was inspired by a simple co-evolutionary model, developed to show the emergence of self-organized criticality in ecosystems. The algorithm is of easy implementation, does not make use of derivatives and can be applied to unconstrained or constrained problems, non-convex or even disjoint design spaces, with any combination of continuous, discrete or integer variables. It is a global search meta-heuristic, like the Genetic Algorithm (GA) and the Simulated Annealing (SA), but with the advantage of having only one free parameter to adjust. The GEO has been shown to be competitive to the GA and the SA in tackling complex design spaces and a useful tool in real design problems. Here the algorithm is described, including a step-by-step implementation to a simple numerical example, its main characteristics highlighted, and its efficacy as a design tool illustrated with an application to satellite thermal design. 701 E. Chocolate Avenue, Suite 200, Hershey PA 17033-1240, USA Tel: 717/533-8845; Fax 717/533-8661; URL-http://www.idea-group.com 16*"" IDEA GROUP PUBLISHING This chapter appears in the book, Recent Developments in Biologically Inspired Computing, edited by Leandro N. de Castro and Fernando J. Von Zuben. Copyright © 2005, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited.