Genetic programming of conventional features to detect seizure precursors

This paper presents an application of genetic programming (GP) to optimally select and fuse conventional features (C-features) for the detection of epileptic waveforms within intracranial electroencephalogram (IEEG) recordings that precede seizures, known as seizure precursors. Evidence suggests that seizure precursors may localize regions important to seizure generation on the IEEG and epilepsy treatment. However, current methods to detect epileptic precursors lack a sound approach to automatically select and combine C-features that best distinguish epileptic events from background, relying on visual review predominantly. This work suggests GP as an optimal alternative to create a single feature after evaluating the performance of a binary detector that uses: (1) genetically programmed features; (2) features selected via GP; (3) forward sequentially selected features; and (4) visually selected features. Results demonstrate that a detector with a genetically programmed feature outperforms the other three approaches, achieving over 78.5% positive predictive value, 83.5% sensitivity, and 93% specificity at the 95% level of confidence.     

A heuristic algorithm for distributed control in manufacturing systems

Distributed Problem Solving Networks (DPSN) provide a means for interconnecting intelligent problem solver nodes that can solve only a part of a problem depending on their ability in the problem domain. The decomposition of a problem into subproblems, and the selection of nodes to solve them can be regarded as the generation of an AND/OR tree, and the solution of the problem as a search for a solution tree. Introducing measurements for the cost of a solution tree, we present an algorithm to find one having minimal cost under certain conditions. A Flexible Manufacturing System consisting of a network of flexible workcells is used as an example.     

深亚微米条件下的多层VLSI／MCM层分配的遗传算法

本文提出了一个深亚微米条件下的多层ＶＬＳＭＣＭ有约束分层层分配的遗传算法。该算法分为两步：首先进行超层分配，使各线网满足Ｃｒｏｓｓｔａｌｋ约束，且超层数目最少；然后进行各超层的通孔最少化二分层。与目前的层分配算法相比，该遗传算法具有目标全面，全局优化能力强等特点，是一种可应用于深亚微米条件下的ＩＣ ＣＡＤ的有效分层方法。     

Response level crossing rate of a linear system excited by a partially specified Gaussian load process

The problem of determining the maximum mean response level crossing rate of a linear system driven by a partially specified Gaussian load process has been considered. The partial specification of the load is given only in terms of its total average energy. The critical input power spectral (PSD) function, which maximizes the mean response level crossing rate, is obtained. The critical input PSD turns out to be highly narrow-banded which fails to capture the erratic nature of the excitation. Consequently, the trade-off curve between the maximum mean response level crossing rate and the maximum disorder in the input process, quantified in terms of its entropy rate, has been generated. The method of Pareto optimization is used to tackle the conflicting objectives of the simultaneous maximization of the mean response level crossing rate and the input entropy rate. The non-linear multi-objective optimization has been carried out using a recently developed multi-criteria genetic algorithm scheme. Illustrative example of determining the critical input of an axially vibrating rod, excited by a partially specified stationary Gaussian load process, has been considered.     

一种遗传-梯度混合算法

本文提出一种函数全局优化的遗传-梯度混合算法。该算法由选择、交叉和诱导变异算子组成。通过对自适应滤波和前向神经网络权值训练两个典型问题的数字仿真，表明该算法是一种快速有效的全局优化算法。     

最小生成树的高效异步并行算法

在ＭＩＭＤ－ＳＭ并行计算模型上，本文给出了时间复杂性为Ｏ（ｎ（ｎ／ｐ＋ｌｏｇｐ））的最小生成树的异步并行算法，其中ｎ，ｐ（１≤ｐ≤ｎ）分别表示图的顶点数和处理机的个数。     

A Search for Hidden Relationships: Data Mining with Genetic Algorithms

This paper presents an algorithm that permits the search for dependencies among sets of data (univariate or multivariate time-series, or cross-sectional observations). The procedure is modeled after genetic theories and Darwinian concepts, such as natural selection and survival of the fittest. It permits the discovery of equations of the data-generating process in symbolic form. The genetic algorithm that is described here uses parts of equations as building blocks to breed ever better formulas. Apart from furnishing a deeper understanding of the dynamics of a process, the method also permits global predictions and forecasts. The algorithm is successfully tested with artificial and with economic time-series and also with cross-sectional data on the performance and salaries of NBA players during the 94–95 season.     

Genetically modified starter and protective cultures

Modern approaches towards starter and protective culture improvement rely on advances in molecular biology. For most microorganisms used for food production, gene technological methods have been well developed. By recombinant DNA technology, ‘tailor-made’ starter and protective cultures may be constructed so as to combine technically desirable features. A single strain which normally would fail to accomplish a given ‘wtask’ may now be improved so as to meet a set of requirements necessary for a specific production or preservation process (e.g. wholesomeness, no off-flavour production, overproduction of bacteriocins or particular enzymes). In addition, undesirable properties (e.g. mycotoxin or antibiotic production by cheese moulds) may be eliminated by techniques such as ‘gene disruption’.     

Machine assignment and part-families formation using group technology

The conventional way of solving the group technology (GT) problem is to start from an assignment of parts to machines and try to find a partitioning of machine cells and part families. The similarity between parts is measured based on commonality of the machines assigned to them. However, parts are assigned to machines based on their operation requirements and the operation capabilities of machines. Similarity between parts should be based on their required operations. In this paper, the authors attempt to solve or facilitate solving the GT problem at the assignment level. An algorithm for assigning parts to machines is provided which utilizes the types of operations required by parts and applies GT principles in producing the assignment. This leads to better partitioning of machine cells and part-families. Furthermore, operation sequences required by parts in determining the similarity between parts have been considered. An algorithm to form part-families based on the operation sequence similarity coefficient has been developed. The resulting families are then used by the assignment algorithm to produce machine assignments to part-families. The use of the algorithm is demonstrated by examples.     

Adaptive solutions to the mutual exclusion problem

Summary Algorithms for mutual exclusion that adapt to the current degree of contention are developed. Afilter and a leader election algorithm form the basic building blocks. The algorithms achieve system response times that are independent of the total number of processes and governed instead by the current degree of contention. The final algorithm achieves a constant amortized system response time. Manhoi Choy was born in 1967 in Hong Kong. He received his B.Sc. in Electrical and Electronic Engineerings from the University of Hong Kong in 1989, and his M.Sc. in Computer Science from the University of California at Santa Barbara in 1991. Currently, he is working on his Ph.D. in Computer Science at the University of California at Santa Barbara. His research interests are in the areas of parallel and distributed systems, and distributed algorithms. Ambuj K. Singh is an Assistant Professor in the Department of Computer Science at the University of California, Santa Barbara. He received a Ph.D. in Computer Science from the University of Texas at Austin in 1989, an M.S. in Computer Science from Iowa State University in 1984, and a B.Tech. from the Indian Institute of Technology at Kharagpur in 1982. His research interests are in the areas of adaptive resource allocation, concurrent program development, and distributed shared memory.A preliminary version of the paper appeared in the 12th Annual ACM Symposium on Principles of Distributed ComputingWork supported in part by NSF grants CCR-9008628 and CCR-9223094