612951
Exploring Multiple Design Topologies Using Genetic Programming And Bond Graphs
To realize design automation of dynamic systems, there are two major issues to be dealt with: open-topology generation of dynamic systems and simulation or analysis of those models. For the first issue, we exploit the strong topology exploration capability of genetic programming to create and evolve structures representing dynamic systems. With the help of ERCs (ephemeral random constants) in genetic programming, we can also evolve the sizing of dynamic system components along with the structures. The second issue, simulation and analysis of those system models, is made more complex when they represent mixed-energy-domain systems. We take advantage of bond graphs as a tool for multi- or mixed-domain modeling and simulation of dynamic systems. Because there are many considerations in dynamic system design that are not completely captured by a bond graph, we would like to generate multiple solutions, allowing the designer more latitude in choosing a model to implement. The approach in this paper is capable of providing a variety of design choices to the designer for further analysis, comparison and trade-off. The approach is shown to be efficient and effective in an example of open-ended real-world dynamic system design application, a printer re-design problem.
19842410
Automated design approaches for multi-domain dynamic systems using bond graphs and genetic programming
8610229
System-Level Synthesis of MEMS via Genetic Programming and Bond Graphs
Initial results have been achieved for automatic synthesis of MEMS system-level lumped parameter models using genetic programming and bond graphs. This paper first discusses the necessity of narrowing the problem of MEMS synthesis into a certain specific application domain, e.g., RF MEM devices. Then the paper briefly introduces the flow of a structured MEMS design process and points out that system-level lumped-parameter model synthesis is the first step of the MEMS synthesis process. Bond graphs can be used to represent a system-level model of a MEM system. As an example, building blocks of RF MEM devices are selected carefully and their bond graph representations are obtained. After a proper and realizable function set to operate on that category of building blocks is defined, genetic programming can evolve both the topologies and parameters of corresponding RF MEM devices to meet predefined design specifications. Adaptive fitness definition is used to better direct the search process of genetic programming. Experimental results demonstrate the feasibility of the approach as a first step of an automated MEMS synthesis process. Some methods to extend the approach are also discussed.
8637956
Dense and Switched Modular Primitives for Bond Graph Model Design
This paper suggests dense and switched modular primitives for a bond-graph-based GP design framework that automatically synthesizes designs for multi-domain, lumped parameter dynamic systems. A set of primitives is sought that will avoid redundant junctions and elements, based on preassembling useful functional blocks of bond graph elements and (optionally) using a switched choice mechanism for inclusion of some elements. Motivation for using these primitives is to improve performance through greater search efficiency and thereby to reduce computational effort. As a proof of concept for this approach, an eigenvalue assignment problem, which is to find bond graph models exhibiting minimal distance errors from target sets of eigenvalues, was tested and showed improved performance for various sets of eigenvalues.
14649563
Structure Fitness Sharing (SFS) For Evolutionary Design By Genetic Programming
Balanced structure and parameter search is critical to evolutionary design with genetic programming (GP). Structure Fitness Sharing (SFS), based on a structure labeling technique, is proposed to maintain the structural diversity of a population and combat premature convergence of structures. SFS achieves balanced structure and parameter search by applying fitness sharing to each unique structure in a population, to prevent takeover by the best structure and thereby maintain the diversity of both structures and parameters simultaneously. SFS does not require definition of a distance metric among structures, and is thus more universal and efficient than other fitness sharing methods for GP. The effectiveness of SFS is demonstrated on a real-world bond-graph-based analog circuit synthesis problem.
9574067
The Hierarchical Fair Competition (HFC) Framework for Sustainable Evolutionary Algorithms
Many current Evolutionary Algorithms (EAs) suffer from a tendency to converge prematurely or stagnate without progress for complex problems. This may be due to the loss of or failure to discover certain valuable genetic material or the loss of the capability to discover new genetic material before convergence has limited the algorithm's ability to search widely. In this paper, the Hierarchical Fair Competition (HFC) model, including several variants, is proposed as a generic framework for sustainable evolutionary search by transforming the convergent nature of the current EA framework into a non-convergent search process. That is, the structure of HFC does not allow the convergence of the population to the vicinity of any set of optimal or locally optimal solutions. The sustainable search capability of HFC is achieved by ensuring a continuous supply and the incorporation of genetic material in a hierarchical manner, and by culturing and maintaining, but continually renewing, populations of individuals of intermediate fitness levels. HFC employs an assembly-line structure in which subpopulations are hierarchically organized into different fitness levels, reducing the selection pressure within each subpopulation while maintaining the global selection pressure to help ensure the exploitation of the good genetic material found. Three EAs based on the HFC principle are tested - two on the even-10-parity genetic programming benchmark problem and a real-world analog circuit synthesis problem, and another on the HIFF genetic algorithm (GA) benchmark problem. The significant gain in robustness, scalability and efficiency by HFC, with little additional computing effort, and its tolerance of small population sizes, demonstrates its effectiveness on these problems and shows promise of its potential for improving other existing EAs for difficult problems. A paradigm shift from that of most EAs is proposed: rather than trying to escape from local optima or delay convergence at a local optimum, HFC allows the emergence of new optima continually in a bottom-up manner, maintaining low local selection pressure at all fitness levels, while fostering exploitation of high-fitness individuals through promotion to higher levels.
2207402
Bias properties of infinitesimal perturbation analysis for systems with parallel servers
Abstract Infinitesimal pertubation analysis (IPA) is a technique for estimating derivatives of performance measures from a single simulation of a stochastic discrete-event system, which might, for example, be modeling a computer/communication network or a manufacturing system. Such derivative estimates are useful in sensitivity analysis of the system and in optimizing—or at least improving—the performance of the system through gradient algorithms. However, it is well-known that IPA gives biased estimates for systems with multiple servers (channels or machines) in parallel when the server characteristics are not identical. In this paper, we investigate the seriousness of the bias both theoretically and experimentally. From our study, we believe the IPA estimator is relatively insensitive to small differences in the service time distributions of the servers. Specifially, we conjecture that for nearly-identical servers, the bias is proportional to the square of the difference between the means of the service time distributions. To support this conjecture, we present a tractable analytical example and investigate, via simulation experiments, more general systems.
6719974
Intrusion Detection Based on Data Mining
52800042
Library cell layout with Alt-PSM compliance and composability
56583323
Exploiting level sensitive latches in wire pipelining
Wire pipelining emerges as a new necessity for global wires due to increasing wire delay, shrinking clock period and growing chip size. Existing approaches on wire pipelining are mostly based on edge triggered flip-flops. In this paper, we demonstrate the advantages of using level sensitive latches in terms of both latency and area cost. The input-output timing coupling and the strict short path constraint for latches demand additional design elaborations compared with flip-flops. New approaches are proposed in this work to solve these difficulties so that the advantages of latches can be fully utilized. In particular, a deferred delay padding technique is developed to correct short path violations with the minimal extra cost. These techniques are integrated with a dynamic programming based concurrent synchronous element and repeater insertion framework. Experimental results confirm the advantages of using latches as well as effectiveness of our algorithms.
38537514
A Biometric Encryption Approach Incorporating Fingerprint Indexing in Key Generation
This paper presents a new biometric encryption protocol in which encryption key is incorporated with fingerprint indexing. Based on the extended chaotic Baker map, the pixel permutation and gray level value substitution are performed to shuffle pixel positions in the original fingerprint images. The encryption key of the pixel permutation is generated by the combination of the random pixel distribution of a fingerprint imprint as well as features used for the fingerprint indexing. In addition to the advantage enjoyed by biometric keys over traditional password/PINs, the proposed biometric encryption approach is very efficient in identity identification within a large database due to the pre-filtering feature of the fingerprint indexing incorporated in the keys. This approach is applicable to the centralized matching scenario where fingerprints need to be encrypted before transmitted. Simulation results have validated the proposed schemes.