Threshold selection-a selection mechanism for noisy evolutionary algorithms-is put into the broader context of hypothesis testing. Theoretical results are presented and applied to a simple model of stochastic search and to a simplified elevator simulator. Design of experiments methods are used to validate the significance of the results.
Abstract. Efficient elevator group control is important for the operation of large buildings. Recent developments in this field include the use of fuzzy logic and neural networks. This paper summarizes the development of an evolution strategy (ES) that is capable of optimizing the neuro-controller of an elevator group controller. It extends the results that were based on a simplified elevator group controller simulator. A threshold selection technique is presented as a method to cope with noisy fitness function values during the optimization run. Experimental design techniques are used to analyze first experimental results.
In this paper we demonstrate the applicability of evolutionary algorithms (EAs) to the optimization of circuit designs. We examine the design of a full-adder cell, and show the capability of design of experiments (DOE) methods to improve the parameter-settings of EAs.
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