Sandor Markon

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The starting point for the analysis and experiments presented in this paper is a simplified elevator control problem, called ’S-ring’. As in many other real-world optimization problems, the exact fitness function evaluation is disturbed by noise. Evolution Strategies (ES) can generally cope with noisy fitness function values. It has been proposed that the(More)
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.
AbsfrucfGenetic Network Programming (GNP) has been proposed as a new method of evolutionary computation. Until now, GNP has been applied to various problems and its effectiveness was clarified. However, these problems were virtual models, so the applicability and availability of GNP to the realworld applications have not been studied. In this paper, as a(More)
The optimization of complex real-world problems might benefit from well tuned algorithm’s parameters. We propose a methodology that performs this tuning in an effective and efficient algorithmical manner. This approach combines methods from statistical design of experiments, regression analysis, design and analysis of computer experiments methods, and(More)
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(More)
Recently, Artificial Intelligence (AI) technology has been applied to many applications. As an extension of Genetic Algorithm (GA) and Genetic Programming (GP), Genetic Network Programming (GNP) has been proposed, whose gene is constructed by directed graphs. GNP can perform a global searching, but its evolving speed is not so high and its optimal solution(More)
Today’s urban life cannot be imagined without elevators. The central part of an elevator system, the elevator group controller, assigns elevator cars to service calls in real-time while optimizing the overall service quality, the traffic throughput, and/or the energy consumption. The elevator supervisory group control (ESGC) problem can be classified as a(More)
Efficient elevator group control is a complex combinatorial optimization problem. Recent developments in this field include the use of reinforcement learning, fuzzy logic, neural networks and evolutionary algorithms [Mar95, CB98]. This paper summarizes the development of a parallel approach based on evolution strategies (ES) that is capable of optimizing(More)