http://www.kumarasastry.com Thu, 18 Feb 2010 05:34:40 +0000 http://wordpress.org/?v=2.9.2 en hourly 1 http://www.kumarasastry.com/2004/12/10/sub-structural-niching-in-non-stationary-environments/ http://www.kumarasastry.com/2004/12/10/sub-structural-niching-in-non-stationary-environments/#comments Fri, 10 Dec 2004 16:00:03 +0000 Kumara Sastry http://kumarasastry.com/?p=42 Sastry, K., Abbass, H. A., Goldberg, D. E. (2004). Proceedings of the Australian Artificial Intelligence Conference. 873—885. [Full paper - PDF] [Full paper - PS].

Abstract:

Niching enables a genetic algorithm (GA) to maintain diversity in a population. It is particularly useful when the problem has multiple optima where the aim is to find all or as many as possible of these optima. When the fitness landscape of a problem changes overtime, the problem is called non-stationary, dynamic or time-variant problem. In these problems, niching can maintain useful solutions to respond quickly, reliably and accurately to a change in the environment. In this paper, we present a niching method that works on the problem substructures rather than the whole solution, therefore it has less space complexity than previously known niching mechanisms. We show that the method is responding accurately when environmental changes occur.

]]> http://www.kumarasastry.com/2004/12/10/sub-structural-niching-in-non-stationary-environments/feed/ 0 http://www.kumarasastry.com/2004/11/24/oiling-the-wheels-of-change-the-role-of-adaptive-automatic-problem-decomposition-in-non-stationary-environments/ http://www.kumarasastry.com/2004/11/24/oiling-the-wheels-of-change-the-role-of-adaptive-automatic-problem-decomposition-in-non-stationary-environments/#comments Thu, 25 Nov 2004 00:41:37 +0000 Kumara Sastry http://kumarasastry.com/?p=41 Abbass, H. A., Sastry, K., Goldberg, D. E. (2004). IlliGAL report no. 2004029. University of Illinois at Urbana-Champaign. [Full paper - PDF] [Full paper - PS].

Abstract:
Genetic algorithms (GAs) that solve hard problems quickly, reliably and accurately are called competent GAs. When the fitness landscape of a problem changes overtime, the problem is called non—stationary, dynamic or time—variant problem. This paper investigates the use of competent GAs for optimizing non-stationary optimization problems. More specifically, we use an information theoretic approach based on the minimum description length principle to adaptively identify regularities and substructures that can be exploited to respond quickly to changes in the environment. We also develop a special type of problems with bounded difficulties to test non-stationary optimization problems. The results provide new insights into non-stationary optimization problems and show that a search algorithm which automatically identifies and exploits possible decompositions is more robust and responds quickly to changes than a simple genetic algorithm.

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