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Novel semiconductor device approach for finding acceptable solutions to classes of graph-based computing problems not otherwise possible

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SOLID-STATE ELECTRONICS 2003年第2期47卷 367-376页

作者： De Claris, N Iliadis, AA Lacaze, A Univ Maryland Dept Elect & Comp Engn College Pk MD 20742 USA Univ Maryland Dept Pathol Sch Med Baltimore MD 21201 USA

The framework and practical applications of ongoing efforts to design novel classes of semiconductor devices making it possible to address highly complex problems that can not be currently solved in real-time, is presented. Important classes of such problems are identified and characterized by massively parallel and distributed graph-based architectures of ever-increasing complexity, national, and global scope. The etiology of the inability of current digital microprocessor-based computing schemes and devices to address such complex real-time problems is briefly reviewed. An overview of novel classes of information processing semiconductor devices, designated as "system-on-a-chip" devices that overcome these limitations, is given. Emanating from the application of a new graph-based methodology, they are designed to find acceptable domain-specific solutions in real-time. Examples and simulation results illustrate key concepts and capabilities of these devices for planning classes of problems. (C) 2002 Elsevier Science Ltd. All rights reserved.

关键词： semiconductor devices system-on-a-chip neural networks graph-based problems planning

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Novel semiconductor device approach for finding acceptable solutions to classes of graph-based computing problems not otherwise possible

Novel semiconductor device approach for finding acceptable s...

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International Semiconductor Device Research Symposium (ISDRS 01)

作者： De Claris, N Iliadis, AA Lacaze, A Univ Maryland Dept Elect & Comp Engn College Pk MD 20742 USA Univ Maryland Dept Pathol Sch Med Baltimore MD 21201 USA

ISBN: (纸本)0780374320

关键词： semiconductor devices system-on-a-chip neural networks graph-based problems planning

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GAPN-LA: A framework for solving graph problems using Petri nets and learning automata

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2019年 77卷 255-267页

作者： Vahidipour, S. M. Esnaashari, M. Rezvanian, A. Meybodi, M. R. Univ Kashan Dept Comp Fac Elect & Comp Engn Kashan Iran KN Toosi Univ Technol Fac Comp Engn Tehran Iran Inst Res Fundamental Sci IPM Sch Comp Sci Tehran Iran Amirkabir Univ Technol Comp Engn & Informat Technol Dept Soft Comp Lab Tehran Iran

A fusion of learning automata and Petri nets, referred to as APN-LA, has been recently introduced in the literature for achieving adaptive Petri nets. A number of extensions to this adaptive Petri net have also been introduced;together we name them the APN-LA family. Members of this family can be utilized for solving problems in the domain of graph problems;each member is suitable for a specific category within this domain. In this paper, we aim at generalizing this family into a single framework, called generalized APN-LA (GAPN-LA), which can be considered as a framework for solving graph-based problems. This framework is an adaptive Petri net, organized into a graph structure. Each place or transition in the underlying Petri net is mapped into exactly one vertex of the graph, and each vertex of the graph represents a part of the underlying Petri net. A vertex in GAPN-LA can be considered as a module, which, in cooperation with other modules in the framework, helps in solving the problem at hand. To elaborate the problem-solving capability of the GAPN-LA, several graph-based problems have been solved in this paper using the proposed framework.

关键词： Adaptive Petri net Learning automata Modular algorithms graph-based problems

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The influence of uncertainties on optimization of vaccinations on a network of animal movements

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SOFT COMPUTING 2021年第6期25卷 4907-4923页

作者： Michalak, Krzysztof Giacobini, Mario Wroclaw Univ Econ Dept Informat Technol Wroclaw Poland Univ Torino Dept Vet Sci Turin Italy

In this article, multiobjective optimization of vaccinations is studied using graph-based modelling and simulations of the spreading of the disease. Real-life dataset of animal movements between farms and pastures in the Piedmont region of Italy is used, from which a dynamic network of contacts is reconstructed. Evolutionary multiobjective optimization of vaccinations is compared with vaccination strategies based on degrees or strengths of graph nodes, number of animals in the farms as well as with the ring vaccination strategy. In the article, the influence of uncertainties represented by the lack of knowledge of initial disease cases and the change of the contacts network by a rewiring process on the vaccination optimization is studied. Results of experiments show that evolutionary optimization of vaccinations can outperform vaccination strategies when enough information is provided. When many disease cases remain unknown or when the changes in the contacts network are large, the performance of the optimization algorithm is adversely affected. Obtained results motivate further research on modelling changes in animal movement patterns, as well as hybrid methods combining evolutionary optimization with vaccination strategies.

关键词： Disease prevention Epidemics control DPEC graph-based problems Combinatorial optimization Multiobjective optimization

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graph-based quadratic optimization: A fast evolutionary approach

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COMPUTER VISION AND IMAGE UNDERSTANDING 2011年第7期115卷 984-995页

作者： Bulo, Samuel Rota Pelillo, Marcello Bomze, Immanuel M. DAIS Univ Ca Foscari Venezia Venice Italy Univ Vienna ISDS A-1010 Vienna Austria

Quadratic optimization lies at the very heart of many structural pattern recognition and computer vision problems, such as graph matching, object recognition, image segmentation, etc., and it is therefore of crucial importance to devise algorithmic solutions that are both efficient and effective. As it turns out, a large class of quadratic optimization problems can be formulated in terms of so-called "standard quadratic programs" (StQPs), which ask for finding the extrema of a quadratic polynomial over the standard simplex. Computationally, the standard approach for attacking this class of problems is to use replicator dynamics, a well-known family of algorithms from evolutionary game theory inspired by Darwinian selection processes. Despite their effectiveness in finding good solutions in a variety of applications, however, replicator dynamics suffer from being computationally expensive, as they require a number of operations per step which grows quadratically with the dimensionality of the problem being solved. In order to avoid this drawback, in this paper we propose a new population game dynamics (INIMDYN) which is motivated by the analogy with infection and immunization processes within a population of "players." We prove that the evolution of our dynamics is governed by a quadratic Lyapunov function, representing the average population payoff, which strictly increases along non-constant trajectories and that local solutions of StQPs are asymptotically stable (i.e., attractive) points. Each step of INIMDYN is shown to have a linear time/space complexity, thereby allowing us to use it as a more efficient alternative to standard approaches for solving StQPs and related optimization problems. Indeed, we demonstrate experimentally that INIMDYN is orders of magnitude faster than, and as accurate as, replicator dynamics on various applications ranging from tree matching to image registration, matching and segmentation. (C) 2011 Elsevier Inc. All rights reserved.

关键词： Quadratic optimization Population dynamics graph-based problems

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Cellular adaptive Petri net based on learning automata and its application to the vertex coloring problem

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DISCRETE EVENT DYNAMIC SYSTEMS-THEORY AND APPLICATIONS 2017年第4期27卷 609-640页

作者： Vahidipour, S. Mehdi Meybodi, Mohammad Reza Esnaashari, Mehdi Univ Kashan Dept Comp Fac Elect & Comp Engn Kashan Iran Inst Res Fundamental Sci IPM Sch Comp Sci POB 19395-5746 Tehran Iran Amirkabir Univ Technol Comp Engn & Informat Technol Dept Tehran Iran KN Toosi Univ Technol Dept Comp Engn Tehran Iran

In a Petri net, a decision point is raised when two or more transitions are simultaneously enabled in a given marking. The decision to be made at such a point is the selection of an enabled transition for firing. Decision making in Petri nets is accomplished by a so called controlling mechanism. Whenever a Petri net is used to represent an algorithm, the application of a different controlling mechanism results in a different instance of that algorithm. Recently, an adaptive controlling mechanism has been introduced for Petri nets in which several learning automata are used as decision makers during the evolution of the Petri nets. A Petri net with this adaptive controlling mechanism is referred to as APN-LA. Using APN-LA, one may be able to design adaptive algorithms to solve problems. There are problems for which designing a single APN-LA is tedious and results in a large and complex model. One class of such problems is the cellular problem, in which an identical algorithm must be executed in each cell and the solution to the problem is generated from the cooperation of these identical algorithms. To avoid having large and complex APN-LAs for cellular problems, a cellular adaptive Petri net called CAPN-LA is proposed in this paper, which consists of a cellular structure and a number of identical APN-LAs. In CAPN-LA, each APN-LA represents the algorithm which must be executed in each cell, and the required cooperation between the neighboring cells is handled by means of cooperation between the APN-LAs in those cells. The notation of expediency for this model is defined, and, using the steady-state analysis of the behavior of the CAPN-LA model, conditions under which this model is expedient are stated. A measure of expediency is defined for comparing different CAPN-LAs according to their expected reward;a CAPN-LA which receives a higher expected reward is regarded as more expedient. The proposed CAPN-LA is then used to design different algorithms for the classic prob

关键词： AdaptivePetri net Learning automata Cellular algorithm graph-based problems Vertex coloring (VC) problem

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Multiobjective Optimization of a Targeted Vaccination Scheme in the Presence of Non-diagnosed Cases 1

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23rd European International Conference on the Applications of Evolutionary Computation (EvoApplications) Held as Part of EvoStar Conference

作者： Michalak, Krzysztof Wroclaw Univ Econ Fac Management Dept Informat Technol Wroclaw Poland

ISBN: (数字)9783030437220

ISBN: (纸本)9783030437213;9783030437220

One of the problems in disease prevention and epidemics control is the large level of uncertainty present in the solved problems. If an optimization of targeted vaccination scheme is attempted, the evaluation of solutions usually involves simulating the non-deterministic spreading of the disease. However, the non-determinism of the epidemic dynamics is just one of the sources of uncertainty in this problem. This paper studies another source of uncertainty in optimization of targeted vaccination schemes, which is the existence of non-diagnosed cases of the disease. In the paper the multiobjective optimization of a targeted vaccination scheme is carried out under an assumption that only a fraction of really existing cases of the disease are known. Solutions found by the optimization algorithm in this limited-knowledge scenario are then evaluated in a simulation in which all existing cases of the disease are taken into account. Optimized solutions are compared with the mass vaccination scheme which is not based on the knowledge of existing cases of the disease. An uncertainty-handling technique is proposed that randomly adds infected individuals to the simulations in order to compensate for the unknown ones.

关键词： Disease prevention Epidemics control DPEC Combinatorial optimization graph-based problems

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