2224019
Using an Immune System Model to Explore Mate Selection in Genetic Algorithms
When Genetic Algorithms (GAs) are employed in multimodal function optimization, engineering and machine learning, identifying multiple peaks and maintaining subpopulations of the search space are two central themes. In this paper, an immune system model is adopted to develop a framework for exploring the role of mate selection in GAs with respect to these two issues. The experimental results reported in the paper will shed more light into how mate selection schemes compare to traditional selection schemes. In particular, we show that dissimilar mating is beneficial in identifying multiple peaks, yet harmful in maintaining subpopulations of the search space.
8079480
A Systematic Study of Genetic Algorithms with Genotype Editing
This paper continues our systematic study of an RNA- editing computational model of Genetic Algorithms (GA). This model is constructed based on several genetic editing characteristics that are gle- aned from the RNA editing system as observed in several organisms. We have expanded the traditional Genetic Algorithm with artificial editing mechanisms as proposed in (11) and (12). The incorporation of editing mechanisms, which stochastically alter the information encoded in the genotype, provides a means for artificial agents with genetic descriptions to gain greater phenotypic plasticity, which may be environmentally re- gulated. The systematic study of this artificial genotype editing model has shed some light into the evolutionary implications of RNA editing and how to select proper genotype editors to design more robust GAs. Our results also show promising applications to complex real-world pro- blems. We expect that the framework here developed will both facilitate determining the evolutionary role of RNA editing in biology, and advance the current state of research in Evolutionary Computation.
8295901
Bounded-latency content distribution feasibility and evaluation
This paper investigates the performance of a content distribution network designed to provide bounded content access latency. Content can be divided into multiple classes with different configurable per-class delay bounds. The network uses a simple distributed algorithm to dynamically select subsets of its proxy servers for different classes such that a global per-class delay bound is achieved on content access. The content distribution algorithm is implemented and tested on PlanetLab, a world-wide distributed Internet testbed. Evaluation results demonstrate that, despite Internet delay variability, subsecond delay bounds (of 200-500 ms) can be guaranteed with a very high probability at only a moderate content replication cost. The distribution algorithm achieves a four to fivefold reduction in the number of response-time violations compared to prior content distribution approaches that attempt to minimize average latency. To the authors' knowledge, this paper presents the first wide-area performance evaluation of an algorithm designed to bound maximum content access latency, as opposed to optimizing an average performance metric.
54735
An architecture for real-time active content distribution
The phenomenal growth of the World Wide Web has made it the most popular Internet application today. Web caching and content distribution services have been recognized as valuable techniques to mitigate the explosion of Web traffic. An increasing fraction of Web traffic today is dynamically generated and therefore intrinsically difficult for replication using present static approaches. Scalable delivery of such active content poses a myriad of challenges, including content replication, update propagation, and consistency management. This paper makes two contributions: (1) it describes a scalable architecture for transparent demand-driven distribution of active content; (2) the system can provide real-time delay guarantee on content access. Our approach involves migrating the scripts which generate the dynamic Web traffic, and their data, from the server to active content distribution proxies nearest to the clients and mechanisms on these proxies to enforce real-time delay guarantees for client requests. Our system is implemented and deployed on PlanetLab, a real-world distributed Internet testbed. Experimental data show that significant improvements are observed in effective throughput and client response time, and delay bounds on content access can be guaranteed with a very high probability.
10023746
Test and diagnosis of word-oriented multiport memories
Conventionally, the test of multiport memories is considered difficult because of the complex behavior of the faulty memories and the large number of inter-port faults. This paper presents an efficient approach for testing and diagnosing multiport RAMs. Our approach takes advantage of the higher access bandwidth due to the increased number of read/write ports, which also provides higher observability and controllability that effectively reduces the test time. Our key idea is that a sequence of March operations for any memory cell can be folded and executed within a single access cycle. We have also developed an efficient test algorithm for port-specific faults as well as traditional cell faults. The port-specific faults include the stuck-open, address decoder, and inter-port faults, for both bit-oriented and word-oriented RAMs. Experimental results for our folding scheme show that the test time reduction is about 28% for a commercial 8 KB embedded SRAM. An efficient diagnostic algorithm is also proposed for the port-specific faults and traditional cell faults.
9038741
Semantic Scoring Based on Small-World Phenomenon for Feature Selection in Text Mining
This paper proposes an effective scoring scheme for feature selection in Text Mining, using characteristics of Small-World Phenomenon on the semantic networks of documents. Our focus is on the reservation of both syntactic and statistical information of words, rather than solely simple frequency summarization in prevailing scoring schemes, such as TFIDF. Experimental results on TREC dataset show that our scoring scheme outperforms the prevailing schemes.
15052014
Optimization of Regular Expression Pattern Matching Circuits on FPGA
Regular expressions are widely used in network intrusion detection system (NIDS) to represent patterns of network attacks. Since traditional software-only NIDS cannot catch up to the speed advance of networks, many previous works propose hardware architectures on FPGA to accelerate attack detection. The challenge of hardware implementation is to accommodate the regular expressions to FPGAs of the large number of attacks. Although the minimization of logic equations has been studied intensively in the CAD area, the minimization of multiple regular expressions has been largely neglected. This paper presents an architecture allowing our algorithm to extract and share common sub-regular expressions. Experimental results show that our sharing scheme significantly reduces the area of regular expression circuits