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Proceedings Paper

Micro-Genetic Algorithms For Stationary And Non-Stationary Function Optimization
Author(s): Kalmanje Krishnakumar
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Paper Abstract

Simple Genetic Algorithms (SGA) have been shown to be useful tools for many function optimization problems. One present drawback of SGA is the time penalty involved in evaluating the fitness functions (performance indices) for large populations, generation after generation. This paper explores a small population approach (coined as Micro-Genetic Algorithms--μGA) with some very simple genetic parameters. It is shown that ,μGA implementation reaches the near-optimal region much earlier than the SGA implementation. The superior performance of the ,μGA in the presence of multimodality and their merits in solving non-stationary function optimization problems are demonstrated.

Paper Details

Date Published: 1 February 1990
PDF: 8 pages
Proc. SPIE 1196, Intelligent Control and Adaptive Systems, (1 February 1990); doi: 10.1117/12.969927
Show Author Affiliations
Kalmanje Krishnakumar, The University of Alabama (United States)

Published in SPIE Proceedings Vol. 1196:
Intelligent Control and Adaptive Systems
Guillermo Rodriguez, Editor(s)

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