this paper, our intention is to explore the applicability and e#ciency of using genetic algorithm techniques in conjunction with approximation models representing response functions with multiple local minima and sharp discontinuities in the multidimensional design optimization of a low-boom supersonic business jet configuration. The goal of this multiobjective design optimization problem is to reduce the sonic boom signature at the ground by modifying the aircraft configuration parameters while preserving or improving aerodynamic performance

Realistic high-dimensional MDO problems are more likely to have multimodal search spaces and they are also mutiobjective in nature. Genetic Alogrithms(GAs) are becoming popular choices for better global and multiobjective optimization frameworks to fully realize the full benefits of conducting MDO. One of the biggest drawbacks of GAs, however, is that they require many function evaluations to achieve a reasonable improvement within the design space. Therefore, the efficiency of GAs has to be improved in some way before they can be truly used in high-fidelity MDO. In this work, a multiobjective design optimization framework is developed by combining GAs and an approximation technique called Kriging method which can produce fairly accurate global approximations to the actual design space to provide the function evaluations efficiently. It is applied to a low boom supersonic business jet design problem and its results demonstrate the efficiency and applicability of the proposed design framework. Furthermore, the possibility of using the Kriging approximation models as computationally inexpensive gradient estimators to accelerate the GA process is investigated. 1.

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Abstract — Pervasive computing calls for applications which are often composed from independent and distributed components using facilities from the environment. This paradigm has evolved into task based computing where the application composition relies on explicit user task descriptions. The composition of applications has to be performed at run-time as the environment is dynamic and heterogeneous due to e.g., mobility of the user. An algorithm that decides on a component set and allocates it onto hosts accordingly to user task preferences and the platform constraints plays a central role in the application composition process. In this paper we will describe an algorithm for task-based application allocation. The algorithm uses micro-genetic approach and is characterized by a very low computational load and good convergence properties. We will compare the performance and the scalability of our algorithm with a straightforward evolutionary algorithm. Besides, we will outline a system for task-based computing where our algorithm is used. I.

have been also applied to improve the difficult tradeoff

We present a new non-dominated sorting algorithm to generate the non-dominated fronts in multi-objective optimization with evolutionary algorithms, particularly the NSGA-II. The non-dominated sorting algorithm used by NSGA-II has a time complexity of O(MN 2) in generating non-dominated fronts in one generation (iteration) for a population size N and M objective functions. Since generating non-dominated fronts takes the majority of total computational time (excluding the cost of fitness evaluations) of NSGA-II, making this algorithm faster will significantly improve the overall efficiency of NSGA-II and other genetic algorithms using non-dominated sorting. The new non-dominated sorting algorithm proposed in this study reduces the number of redundant comparisons existing in the algorithm of NSGA-II by recording the dominance information among solutions from their first comparisons. By utilizing a new data structure called the dominance tree and the divide-and-conquer mechanism, the new algorithm is faster than NSGA-II for different numbers of objective functions. Although the number of solution comparisons by the proposed algorithm is close to that of NSGA-II when the number of objectives becomes large, the total computational time shows that the proposed algorithm still has better efficiency because of the adoption of the dominance tree structure and the divide-and-conquer mechanism.

Abstract — We present a tactile intelligent sensory substitution system (TIS 3) as a novel tactile communication system with dialog-based tuning. TIS 3 consists of a tactile encoder (TE) which maps desired objects or patterns (P) onto spatio-temporal stimulation patterns (P’) as a parallel stream of stimulation time courses, by means of a tactile stimulator array (TS) at selected skin location to elicit tactile perceptions (P ∗). The human subject evaluates and compares a percept P ∗ with a given object P as association goal. A Learning Module (LM) for dialog based TEtuning transforms these evaluations into TE-change signals. In a first step, the application of a dialog-based TE-tuning was successfully tested using a TS version with fifteen stimulators on the lower arm for regaining the corresponding stimulation pattern P ’ for a given tactile reference percept P ∗ ref. Optimization of P ∗ was achieved in less than 100 iteration steps by means of a micro evolutionary learning algorithm (MEA). I.