14747412
Adaptive Hierarchical Fair Competition (AHFC) Model For Parallel Evolutionary Algorithms
The HFC model for parallel evolutionary computation is inspired by the stratified competition often seen in society and biology. Subpopulations are stratified by fitness. Individuals move from low-fitness to higher-fitness subpopulations if and only if they exceed the fitness-based admission threshold of the receiving subpopulation, but not of a higher one. The HFC model implements several critical features of a competent parallel evolutionary computation model, simultaneously and naturally, allowing rapid exploitation while impeding premature convergence. The AHFC model is an adaptive version of HFC, extending it by allowing the admission thresholds of fitness levels to be determined dynamically by the evolution process itself. The effectiveness of the Adaptive HFC model is compared with the HFC model on a genetic programming-based evolutionary synthesis example.
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.
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.
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.
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.
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.
6735188
Efficient model update for general link-insertion networks
Link insertion has been proposed as a means of incremental design to improve performance robustness of linear passive networks. In clock network design, links can be inserted between subnetworks to reduce the variability of clock skews introduced by process and environmental fluctuations, thereby improving the network's immunity to PVT variations. Under these scenarios, it is desired to incrementally compute a reduced-order model for the updated network in order to efficiently evaluate the effectiveness of link insertions. In this paper, we present an efficient model update scheme for general link-insertion networks. By updating the Krylov projection subspace used in model order reduction, the proposed scheme can efficiently compute a reduced-order model for the network with inserted links. More generally, we extend the proposed approach to consider the merging of a (small) multiple-input linear network with a much larger network. We demonstrate the usage of the proposed technique for clock networks and general RLC circuits with an arbitrary number of link insertions as well as the more general case where the inserted links are in the form of a linear network
18592986
Simulation Study of TCP Performance Over Mobile IPV4 and Mobile IPV6
Mobile IPv6 (MIPv6) is a protocol to deal with mobility for the next generation Internet (IPv6). However, the performance of MIPv6 has not yet been extensively investigated. Knowledge of how MIPv6 affects TCP performance, especially in comparison with MIPv4, can provide directions for further improvement. In this report, an intensive simulation study of TCP performance over MIPv4 and MIPv6 has been conducted. Simulation using the famous network simulator NS-2 will be used to highlight differences when TCP is used in hybrid wireless environment, over these two Mobile IP protocols. Initial simulation has shown a solid improvement in performance for MIPv6 when IPv6 Route Optimisation features are used. During the course of simulation, a consistent event causing dropped TCP throughput was identified. Out of order arrival of packets would occur when the mobile node initiated a handover. This out of order arrival invokes TCP congestion control falsely which reduces throughput. The difference in overall throughput of MIPv4 compared to MIPv6 is roughly proportional to the difference in packet size attributed to IPv6’s increased header size. Another contribution of this work is to provide modifications and new functions such as node processing time, to the NS-2 simulator to make such investigation possible. To the best of our knowledge, no similar publication has been reported.
6719974
Intrusion Detection Based on Data Mining
18760651
Modelling of SACK TCP and application to the HTTP file transfer environment
It is known that analytic modelling for TCP latency is a non trivial task. Recently, some significant progress has been made, such as the comprehensive result by Sikdar et al. However, models similar to these often rely on trial and error methods such as "data fitting". This can lead to a very limited scope for the resulting model and also large estimation error. In this paper, we propose improvements to Sikdar's SACK TCP model. A new delayed acknowledgement slow start model is developed that is analytically derived from the slow start algorithm which provides a novel mechanism to model the relationship between RTT and the delayed acknowledgement timer. We introduce a simple mechanism to include time taken to send an HTTP get request to broaden the scope of our SACK TCP model to Website file transfer. Simulation and live Internet experimentation has validated our scheme
9430332
Analyzing data reuse for cache reconfiguration
Classical compiler optimizations assume a fixed cache architecture and modify the program to take best advantage of it. In some cases, this may not be the best strategy because each nest might work best with a different cache configuration and transforming a nest for a given fixed cache configuration may not be possible due to data and control dependences. Working with a fixed cache configuration can also increase energy consumption in loops where the best required configuration is smaller than the default (fixed) one. In this paper, we take an alternate approach and modify the cache configuration for each nest, depending on the access pattern exhibited by the nest. We call this technique compiler-directed cache polymorphism (CDCP). More specifically, in this paper, we make the following contributions. First, we present an approach for analyzing data reuse properties of loop nests. Second, we give algorithms to simulate the footprints of array references in their reuse space. Third, based on our reuse analysis, we present an optimization algorithm to compute the cache configurations for each loop nest. Our experimental results show that CDCP is very effective in finding the near-optimal data cache configurations for different nests in array-intensive applications.