Logical process partitioning in distributed simulation using genetic algorithms

This paper presents a method to partition models in logical processes in the context of distributed simulation. The proposed method uses genetic algorithms to decide on the viability and the partitioning technique most indicated. The input parameters to the genetic algorithm are information about the model (number of elements, communication, arrival and service taxes), and the architecture where the simulation is executed. As result, we have the number of logical processes and their mapping on the distributed environment. Two models were used to evaluate the proposed method.     

Wire-antenna designs using genetic algorithms

There is a large class of electromagnetic radiators designated as wire antennas. As a rule, an inductive process is used to design these antennas. Either an integral equation is formulated or a simulator is used that gives the current distributions on the wires of the antenna, from which the electromagnetic properties of the antenna can then be determined. Once the antenna properties are known, the parameters are optimized, using guides such as intuition, experience, simplified equations, or empirical studies. However, using an electromagnetics simulator in conjunction with a genetic algorithm (GA), it is possible to design an antenna using a completely deductive approach: the desired electromagnetic properties of the antenna are specified, and the wire configuration that most closely produces these results is then synthesized by the algorithm. In this paper, we describe four antennas designed using GAs. The first is a monopole, loaded with a modified folded dipole that was designed to radiate uniform power over the hemisphere at a frequency of 1.6 GHz. The second antenna consists of seven wires, the locations and lengths of which are determined by the GA alone, that radiates waves with right-hand-circular polarization at elevation angles above 10°, also at 1.6 GHz. The last two antennas are modified Yagis. One is designed for a broad frequency band and very low sidelobes at a center frequency of 235 MHz. The other is designed for high gain at a single frequency of 432 MHz. We have built and tested these antennas     

Thinned arrays using genetic algorithms

Large arrays are difficult to thin in order to obtain low sidelobes. Traditional statistical methods of aperiodic array synthesis fall far short of optimum configurations. Traditional optimization methods are not well suited for optimizing a large number of parameters or discrete parameters. This paper presents how to optimally thin an array using genetic algorithms. The genetic algorithm determines which elements are turned off in a periodic array to yield the lowest maximum relative sidelobe level. Simulation results for 200 element linear arrays and 200 element planar arrays are shown. The arrays are thinned to obtain sidelobe levels of less than -20 dB. The linear arrays are also optimized over both scan angle and bandwidth.<>     

Post-fabrication clock-timing adjustment using genetic algorithms

To solve the problem of fluctuations in clock timing (also known as "clock skew" problems), we propose an approach for the implementation of post-fabrication clock-timing adjustment utilizing genetic algorithms (GAs). This approach is realized by the combination of dedicated adjustable circuitry and adjustment software, with the values for multiple programmable delay circuits inserted into the clock lines being determined by the adjustment software after fabrication. The proposed approach has three advantages: 1) enhancement in clock frequencies leading to improved operational yields; 2) lower power supply voltages, while maintaining operational yield; and 3) reductions in design times. Two different LSIs have been developed: the first is a programmable delay circuit, developed as an element of the clock-timing adjustment, while the second is a medium-scale circuit, developed to evaluate these advantages in a real chip. Experiments with these two LSIs, as well as a design experiment, have demonstrated these advantages with an enhancement in clock frequency of 25% (max), a reduction in the power-supply voltage of 33%, and a 21% shorter design time.     

Passive filter design using genetic algorithms

The objective of this paper is to propose a new approach for designing a passive LC filter of the full-bridge rectifier by using genetic algorithms (GAs). The performance of the cost-effective passive LC filter for a constant load depends on the appropriate inductor and capacitor selection. Several design methods are reviewed and a novel design methodology using GAs is proposed in this paper. By using the proposed GA program, designer can quickly find appropriate parameter values to meet the desired circuit performance. Experimental results show that an appropriate combination of the inductor and capacitor selected by the proposed GA program can meet the desired power quality requirement. Different cases of design examples are shown in this paper to verify the performance of the proposed design methodology.     

Adhoc-like routing in wired networks with genetic algorithms

Routing of packets in networks requires that a path be selected either dynamically while the packets are being forwarded, or statically (in advance) as in source routing from a source node to a destination. Quality of service (QoS) driven routing has been proposed using a protocol called the “Cognitive Packet Network” (CPN) which dynamically selects paths through a store and forward packet network so as to provide best effort QoS to route peer-to-peer connections. CPN operates very much as an adhoc protocol within a wired setting, and uses smart packets to select routes based on QoS requirements. We extend the path discovery process in CPN to include a genetic algorithm which can help discover new paths that may not have been discovered by smart packets. We describe how possible routes can “evolve” from prior knowledge, and then be selected based on “fitness” with respect to QoS. We detail the design of the algorithm and of its implementation, and report on resulting QoS measurements.     

A genetic algorithm-based clustering approach for database partitioning

In a typical distributed/parallel database system, a request mostly accesses a subset of the entire database. It is, therefore, natural to organize commonly accessed data together and to place them on nearby, preferably the same, machine(s)/site(s). For this reason, data partitioning and data allocation are performance critical issues in distributed database application design. We are dealing with data partitioning. Data partitioning requires the use of clustering. Although many clustering algorithms have been proposed, their performance has not been extensively studied. Moreover, the special problem structure in clustering is rarely exploited. We explore the use of a genetic search-based clustering algorithm for data partitioning to achieve high database retrieval performance. By formulating the underlying problem as a traveling salesman problem (TSP), we can take advantage of this particular structure. Three new operators for GAs are also proposed and experimental results indicate that they outperform other operators in solving the TSP. The proposed GA is applied to solve the data-partitioning problem. Our computational study shows that our GA performs well for this application.     

Channel assignment in cellular radio using genetic algorithms

The channel assignment problem has become increasingly important in mobile telephone communication. Since the usable range of the frequency spectrum is limited, the optimal assignment problem of channels has become increasingly important. Recently Genetic Algorithms (GAs) have been proposed as new computational tools for solving optimization problems. GAs are more attractive than other optimization techniques, such as neural networks or simulated annealing, since GAs are generally good at finding an acceptably good global optimal solution to a problem very quickly. In this paper, a new channel assignment algorithm using GAs is proposed. The channel assignment problem is formulated as an energy minimization problem that is implemented by GAs. Appropriate GAs operators such as reproduction, crossover and mutation are developed and tested. In this algorithm, the cell frequency is not fixed before the assignment procedures as in the previously reported channel assignment algorithm using neural networks. The average generation numbers and the convergence rates of GAs are shown as a simulation result. When the number of cells in one cluster are increased, the generation numbers are increased and the convergence rates are decreased. On the other hand, with the increased minimal frequency interval, the generation numbers are decreased and the convergence rates are increased. The comparison of the various crossover and mutation techniques in a simulation shows that the combination of two points crossover and selective mutation technique provides better results. All three constraints are also considered for the channel assignments: the co-channel constraint, the adjacent channel constraint and the co-site channel constraint. The goal of this paper is the assignment of the channel frequencies which satisfied these constraints with the lower bound number of channels.     

Optimization of lithographic masks using genetic algorithms

Due to its cost effectiveness and reliability, wet-chemical etching of silicon is still one of the key technologies for producing bulk-silicon microstructures. In this paper we present an approach for the design of advanced mask sets for anisotropic, wet-chemical etching of silicon. The optimization method of genetic algorithms is used to derive suitable masks for cases where geometrically calculated compensation structures fail. The underlying etch simulation is described as well as the optimization algorithm itself. Design tasks of current research projects are used as examples to illustrate the advantage of using the presented tool. Udo Triltsch was born in Bergisch Gladbach, Germany, in 1976. He received the Dipl.-Ing. degree for Mechanical Engineering from the Technical University of Braunschweig, Germany, in 2002. He is currently working towards his Ph.D. at the Institute for Microtechnology, Braunschweig, Germany. His research interests include: design methodology for MEMS, process simulation and knowledge management. Anurak Phataralaoha was born in Bangkok, Thailand, in 1973. He received the B. Eng. degree for Production Engineering from KMUTT, Thailand in 1995 and Dipl.-Ing. degree for Mechanical Engineering from Technical University of Clausthal, Germany in 2002. He is currently working towards his Ph.D. at the Institute for Microtechnology, Braunschweig, Germany. His research interests include: 3D-tactile sensors, micro machining for silicon, Tribological micro guide. Stephanus Büttgenbach obtained the Diploma and Ph.D. degrees in physics from the University of Bonn, Germany, in 1970 and 1973, respectively. From 1974 to 1985, he was with the Institute of Applied Physics of the University of Bonn, working on atomic and laser spectroscopy. In 1983, he was promoted to Professor of Physics. From 1977 to 1985, he was also a Scientific Associate at CERN in Geneva, Switzerland. In 1985, Dr. Büttgenbach joined the Hahn-Schickard-Society of Applied Research at Stuttgart as Head of the Department of Microtechnology, where he worked on micromechanics, laser microfabrication, and resonant sensors. From 1988 to 1991, he was the Founding Director of the Institute of Micro and Information Technology of the Hahn-Schickard-Society. In 1991, Dr. Büttgenbach became Professor of Microtechnology at the Technical University of Braunschweig. His current research centers on the development and application of micro sensors, micro actuators, and micro systems. Currently, he is Vice President of the Technical University of Braunschweig, where his areas of responsibility are research and technology transfer. Dima Straube was born in Berlin, Germany, in 1977. He received the Dipl.-Ing. degree for Civil Engineering from Technical University of Berlin, Germany, in 2002. He is currently working towards his Ph.D. at the Institute for Engineering Design, Braunschweig. His research interests include: design methodology for MEMS, computer aided design and tolerance management. Hans-Joachim Franke was born in Helmstedt, Germany, on February 14, 1944. He received his diploma in mechanical engineering (Dipl.-Ing.) from the Technical University of Braunschweig, Germany, in 1969. From 1969 to 1976 he was research assistant of Prof. Roth at the Institute for Engineering Design. In 1976 he received his Ph.D. degree in mechanical engineering. From 1976 to 1988 he had diverse executive positions at the KSB-AG in Frankenthal, Germany, a company, which produces pumps and valves. Since 1988 he has been the director of the Institute for Engineering Design of the Technical University of Braunschweig. His research interests are in the areas of design methodology, computer aided design and machine elements.     

Synthesis of antenna arrays using genetic algorithms

In this article, two particular methods for the synthesis of the complex radiation pattern of a linear and a planar array, based on genetic algorithms (GAs), are discussed. First, we present a combination of Schelkunoff's method and GAs for linear arrays with arbitrary radiation patterns; then, we consider the synthesis of planar arrays with rectangular cells. The results show the flexibility of GAs to solve complex problems related to antenna synthesis, subject to many restrictions which are difficult to treat by classical optimization techniques. There is good agreement between the desired and calculated radiation patterns     

Optical orthogonal code design using genetic algorithms

A construction technique for optical orthogonal codes (OOCs) using genetic algorithms (GAs) is proposed. The performance of the GA OOCs is compared to that of four existing OOCs. Results show that the GA OOCs have a lower probability of error     

Design of Yagi-Uda antennas using genetic algorithms

A method of using genetic algorithms to optimize the element spacing and lengths of Yagi-Uda antennas is presented. A method of moments code, NEC2, performs the task of evaluating each of the antenna designs generated by the genetic algorithm (GA) during the optimization process. To illustrate the capabilities of the method, the length and spacing of several Yagi-Uda antennas are optimized for various performance characteristics. The results are compared to published results from other optimization techniques and to well-designed equally spaced arrays     

Automatic facial feature extraction by genetic algorithms

An automatic facial feature extraction algorithm is presented. The algorithm is composed of two main stages: the face region estimation stage and the feature extraction stage. In the face region estimation stage, a second-chance region growing method is adopted to estimate the face region of a target image. In the feature extraction stage, genetic search algorithms are applied to extract the facial feature points within the face region. It is shown by simulation results that the proposed algorithm can automatically and exactly extract facial features with limited computational complexity.     

An introduction to genetic algorithms for electromagnetics

This article is a tutorial on using genetic algorithms to optimize antenna and scattering patterns. Genetic algorithms are “global” numerical-optimization methods, patterned after the natural processes of genetic recombination and evolution. The algorithms encode each parameter into binary sequences, called a gene, and a set of genes is a chromosome. These chromosomes undergo natural selection, mating, and mutation, to arrive at the final optimal solution. After providing a detailed explanation of how a genetic algorithm works, and a listing of a MATLAB code, the article presents three examples. These examples demonstrate how to optimize antenna patterns and backscattering radar-cross-section patterns. Finally, additional details about algorithm design are given     

Using genetic algorithms for album page layouts

We describe a system that uses a genetic algorithm to interactively generate personalized album pages for visual content collections on the Internet. The system has three modules: preprocessing, page creation, and page layout. We focus on the details of the genetic algorithm used in the page-layout task.     

遗传算法在控制率参数辨识中的应用

为解决现阶段飞机控制律设计和调整很大程度上依赖于设计人员自身经验的问题.提出了一种应用遗传算法辨识控制率参数的方法.首先以国产某型号飞机为研究对象设计爬升控制律,然后应用遗传算法,对控制率中的未知参数构造合适的解码公式并且设定相应的适应度函数,最后基于机载先验飞行数据进行运算.通过判定适应度函数值,得到优化后的未知参数.仿真结果表明,飞机爬升至巡航后,俯仰角与空速稳定,巡航高度偏离目标高度不超过1.5%,这种设计方法得到了比较高的控制精度,能够满足实际应用的需要.     

Harmonic optimization of multilevel converters using genetic algorithms

In this letter, a genetic algorithm (GA) optimization technique is applied to determine the switching angles for a cascaded multilevel inverter which eliminates specified higher order harmonics while maintaining the required fundamental voltage. This technique can be applied to multilevel inverters with any number of levels. As an example, in this paper a seven-level inverter is considered, and the optimum switching angles are calculated offline to eliminate the fifth and seventh harmonics. These angles are then used in an experimental setup to validate the results.     

Pseudocoevolutionary genetic algorithms for power electronic circuits optimization

This correspondence presents pseudocoevolutionary genetic algorithms (GAs) for power electronic circuit (PEC) optimization. Circuit parameters are optimized through two parallel coadapted GA-based optimization processes for the power conversion stage (PCS) and feedback network (FN), respectively. Each process has tunable and untunable parametric vectors. The best candidate of the tunable vector in one process is migrated into the other process as an untunable vector through a migration controller, in which the migration strategy is adaptively controlled by a first-order projection of the maximum and minimum bounds of the fitness value in each generation. Implementation of this method is suitable for systems with parallel computation capacity, resulting in considerable improvement of the training speed. Optimization of a buck regulator for meeting requirements under large-signal changes and at steady state is illustrated. Simulation predictions are verified with experimental results.