Micro-Genetic Algorithms For Stationary And Non-Stationary Function Optimization

Kalmanje Krishnakumar

doi:10.1117/12.969927

1 February 1990 Micro-Genetic Algorithms For Stationary And Non-Stationary Function Optimization

Kalmanje Krishnakumar

Proceedings Volume 1196, Intelligent Control and Adaptive Systems; (1990) https://doi.org/10.1117/12.969927
Event: 1989 Symposium on Visual Communications, Image Processing, and Intelligent Robotics Systems, 1989, Philadelphia, PA, United States

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.

Citation Download Citation

Kalmanje Krishnakumar "Micro-Genetic Algorithms For Stationary And Non-Stationary Function Optimization", Proc. SPIE 1196, Intelligent Control and Adaptive Systems, (1 February 1990); https://doi.org/10.1117/12.969927

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