scholarly journals SEGMA: A Simulated Evolution Gate-Matrix Layout Algorithm

VLSI Design ◽  
1994 ◽  
Vol 2 (3) ◽  
pp. 241-257 ◽  
Author(s):  
Chi-Yu Mao ◽  
Yu Hen Hu
Keyword(s):  
Biological Evolution ◽  
Poor Quality ◽  
Simulated Evolution ◽  
Layout Problem ◽  
Placement Problem ◽  
Layout Algorithm ◽  
Logic Modules ◽  
Gate Matrix ◽  
The Given ◽  
Heuristic Criterion

In this paper, we present a Simulated Evolution Gate Matrix layout Algorithm (SEGMA) for synthesizing CMOS random logic modules. The gate-matrix layout problem is solved as a one-dimensional transistor gates placement problem. Given a placement of all the transistor gates, simulated evolution offers a systematic method to improve the quality of the layout that is measured by the number of tracks needed for the given netlist. This is accomplished by identifying a subset of gates whose relative placements are deemed “poor quality” according to a heuristic criterion. By rearranging the placement of these identified subsets of gates, it is hoped that a gate placement with better quality, meaning fewer tracks, may emerge. Since this method enables the current “generation” of gate placement to evolve into a more advanced one in a way similar to the biological evolution process, this method is called simulated evolution. To apply simulated evolution to solve the gate-matrix layout problem, we propose a novel heuristic criterion, called randomized quality factor, which facilitates the judicious selection of the subset of poor quality gates. Several carefully devised and tested strategies are also implemented. Extensive simulation results indicate that SEGMA is producing very compact gate-matrix layouts.

Genetic Programming

2011 ◽  
pp. 926-931 ◽  
Author(s):  
William H. Hsu
Keyword(s):  
Genetic Programming ◽  
Programming Languages ◽  
Genetic Material ◽  
Biological Evolution ◽  
Validation Data ◽  
Functional Programming Languages ◽  
Real World Problem ◽  
Problem Solving Environment ◽  
Fitness Criterion ◽  
The Given

Genetic programming (GP) is a sub-area of evolutionary computation first explored by John Koza (1992) and independently developed by Nichael Lynn Cramer (1985). It is a method for producing computer programs through adaptation according to a user-defined fitness criterion, or objective function. Like genetic algorithms, GP uses a representation related to some computational model, but in GP, fitness is tied to task performance by specific program semantics. Instead of strings or permutations, genetic programs are most commonly represented as variable-sized expression trees in imperative or functional programming languages, as grammars (O’Neill & Ryan, 2001), or as circuits (Koza et al., 1999). GP uses patterns from biological evolution to evolve programs: • Crossover: Exchange of genetic material such as program subtrees or grammatical rules • Selection: The application of the fitness criterion to choose which individuals from a population will go on to reproduce • Replication: The propagation of individuals from one generation to the next • Mutation: The structural modification of individuals To work effectively, GP requires an appropriate set of program operators, variables, and constants. Fitness in GP is typically evaluated over fitness cases. In data mining, this usually means training and validation data, but cases can also be generated dynamically using a simulator or directly sampled from a real-world problem solving environment. GP uses evaluation over these cases to measure performance over the required task, according to the given fitness criterion.

scholarly journals Obstruction set isolation for the gate matrix layout problem

1994 ◽  
Vol 54 (2-3) ◽  
pp. 169-213 ◽  
Author(s):  
Nancy G. Kinnersley ◽  
Michael A. Langston
Keyword(s):  
Layout Problem ◽  
Gate Matrix

scholarly journals An On-Path Caching Scheme Based on the Expected Number of Copies in Information-Centric Networks

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1705
Author(s):  
Yuanhang Li ◽  
Jinlin Wang ◽  
Rui Han
Keyword(s):  
Network Architectures ◽  
Expected Number ◽  
Placement Problem ◽  
Mobility Support ◽  
Allocation Method ◽  
Caching Scheme ◽  
Content Popularity ◽  
Content Placement ◽  
The Given ◽  
Cache Allocation

The Information-Centric Network (ICN) is one of the most influential future network architectures and in-network caching in ICN brings some helpful features, such as low latency and mobility support. How to allocate cache capacity and place content properly will greatly influence the performance of ICN. This paper focuses on the cache allocation problem and content placement problem under the given cache space budget. Firstly, a lightweight allocation method utilizing information of both topology and content popularity is proposed, to allocate cache space and get the expected number of copies of popular content. The expected number of copies represents the number of content copies placed in the topology. Then, an on-path caching scheme based on the expected number of copies is proposed to handle the content placement problem. In the cache allocation scenario, the lightweight allocation method performs better than other baseline methods. In the content placement scenario, Leave Copy Down (LCD) based on the expected number of copies performs the second-best and is very close to Optimal Content Placement (OCP).

scholarly journals A Parallel Biased Random-Key Genetic Algorithm with Multiple Populations Applied to Irregular Strip Packing Problems

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Bonfim Amaro Júnior ◽  
Plácido Rogério Pinheiro ◽  
Pedro Veras Coelho
Keyword(s):  
Genetic Algorithm ◽  
Packing Problem ◽  
Benchmark Problems ◽  
Strip Packing ◽  
Layout Problem ◽  
Multiple Populations ◽  
Packing Problems ◽  
Free Region ◽  
Positioning Method ◽  
The Given

The irregular strip packing problem (ISPP) is a class of cutting and packing problem (C&P) in which a set of items with arbitrary formats must be placed in a container with a variable length. The aim of this work is to minimize the area needed to accommodate the given demand. ISPP is present in various types of industries from manufacturers to exporters (e.g., shipbuilding, clothes, and glass). In this paper, we propose a parallel Biased Random-Key Genetic Algorithm (µ-BRKGA) with multiple populations for the ISPP by applying a collision-free region (CFR) concept as the positioning method, in order to obtain an efficient and fast layout solution. The layout problem for the proposed algorithm is represented by the placement order into the container and the corresponding orientation. In order to evaluate the proposed (µ-BRKGA) algorithm, computational tests using benchmark problems were applied, analyzed, and compared with different approaches.

scholarly journals Data Cleaning in Cloud Platform

2020 ◽  
Vol 9 (1) ◽  
pp. 2535-2539
Keyword(s):  
Data Cleaning ◽  
Data Association ◽  
Poor Quality ◽  
Quality Data ◽  
Quality Of Data ◽  
Useful Knowledge ◽  
Big Data Applications ◽  
Poor Quality Data ◽  
Free Data ◽  
The Given

: Data is very valuable and it is generated in large volumes. The Use of high-quality data for making quality decisions has become a huge task which helps people to make better decisions, analysis, predictions. We are surrounded by data with errors, Data cleaning is a delayed, complicated task and considered costly. Data polishing is important since it is necessary to remove errors from the data before transferring to the data warehouse since poor quality data is eliminated to get the desired results. The Error-free data will produce precise and accurate results when queried. Hence consistent and proper data is required for the decision making. The characteristics of data polishing is data repairing and data association. Identifying the homogeneous object and linking it to the most associated object is defined as Association. The process of making the database reliable by repairing and finding the faults is defined as repairing. In the case of big data applications, we do not use all the existing data, we use only subsets of appropriate data. Association is the process of converting extensive amounts of raw data to subsets of appropriate data that are useful. Once we get the appropriate data, the available data is analyzed and it leads to knowledge [14]. Multiple approaches are used to associate the given data and to achieve meaningful and useful knowledge to fix or repair [12]. Maintaining polished quality of data is referred to as data polishing. Usually the objectives of data polishing are not properly defined. This paper will discuss the goals of data cleaning and different approaches for data cleaning platforms

Extended Telemedical Consultation using Arden Syntax Based Decision Support, Hypertext and WWW Technique

1997 ◽  
Vol 36 (02) ◽  
pp. 108-114 ◽  
Author(s):  
C. Ekdahl ◽  
O. Wigertz ◽  
N. Shahsavar ◽  
H. Gill ◽  
U. Forsum ◽  
...  
Keyword(s):  
Health Care ◽  
Decision Support ◽  
Clinical Medicine ◽  
Expert Knowledge ◽  
Local Network ◽  
Rural Medicine ◽  
Knowledge Based ◽  
Logic Modules ◽  
The Given ◽  
Arden Syntax

Abstract:There is an obvious need for geographic distribution of expert knowledge among several health care units without increasing the cost of on-site expertise in locations where health care is provided. This paper describes the design of a knowledge-based decision-support system for extended consultation in clinical medicine. The system is based on Arden Syntax for Medical Logic Modules and hypertext using World Wide Web technology. It provides advice and explanations regarding the given advice. The explanations are presented in a hypertext format allowing the user to browse related information and to verify the relevance of the given advice. The system is intended to be used in a closed local network. With special precautions regarding issues of safety and patient security, the system can be used over wider areas such as in rural medicine. A prototype has been developed in the field of clinical microbiology and infectious diseases regarding infective endocarditis.

Study of Crew Scheduling System for High-Speed Railway

2014 ◽  
Vol 667 ◽  
pp. 72-75 ◽  
Author(s):  
Bin Guo ◽  
Lei Shan Zhou ◽  
Dan Dan Li
Keyword(s):  
Computer Technology ◽  
High Speed ◽  
Optimization Problems ◽  
Operating Cost ◽  
Biological Evolution ◽  
Crew Scheduling ◽  
Prototype System ◽  
Engineering Optimization ◽  
Simulated Evolution ◽  
High Speed Railway

We discussed the crew scheduling rules for High-speed railway in China. These rules are mainly relates to crew members and railway operators. One of the aims to establish the rules is to protect the legitimate rights and interests of crew members, and the other is to reduce the operating cost as possible. The idea of biological evolution for engineering optimization problems has been developed for years, the applicability of different algorithms were compared to solve this problem. It shows that simulated evolution method is more applicable and then discussed the main steps of using method SE method to deal with this problem. Finally, based on the computer technology, we designed a prototype system.

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