지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한전기학회 Journal of Electrical Engineering & Technology Journal of Electrical Engineering & Technology Vol.3 No.1
- 발행연도
- 2008.3
- 수록면
- 43 - 51 (9page)
이용수
초록· 키워드
오류제보하기
Evolutionary Algorithms (EAs) cover all the applications involving the use of Evolutionary Computation in electronic system design. It is largely applied to complex optimization problems. EAs introduce a new idea for automatic design of electronic systems; instead of imagine model, abstractions, and conventional techniques, it uses search algorithm to design a circuit.
In this paper, a method for automatic optimization of the digital circuit design method has been introduced. This method is based on randomized search techniques mimicking natural genetic evolution. The proposed method is an iterative procedure that consists of a constant-size population of individuals, each one encoding a possible solution in a given problem space.
The structure of the circuit is encoded into a one-dimensional genotype as represented by a finite string of bits. A number of bit strings is used to represent the wires connection between the level and 7 types of possible logic gates; XOR, XNOR, NAND, NOR, AND, OR, NOT 1, and NOT 2. The structure of gates are arranged in an m * n matrix form in which m is the number of input variables.
In this paper, a method for automatic optimization of the digital circuit design method has been introduced. This method is based on randomized search techniques mimicking natural genetic evolution. The proposed method is an iterative procedure that consists of a constant-size population of individuals, each one encoding a possible solution in a given problem space.
The structure of the circuit is encoded into a one-dimensional genotype as represented by a finite string of bits. A number of bit strings is used to represent the wires connection between the level and 7 types of possible logic gates; XOR, XNOR, NAND, NOR, AND, OR, NOT 1, and NOT 2. The structure of gates are arranged in an m * n matrix form in which m is the number of input variables.
목차
Abstract
1. Introduction
2. Methodology
3. Experiment Results
4. Conclusion
Reference
참고문헌 (11)
참고문헌 신청
1. [단행본] - David, E. Goldberg / 1989 / Genetic Algorithm in Search, Optimization and Machine Learning / Addison-Wesley
2. [단행본] - Holland, J. H. / 1992 / Adaptation in Natural and Artificial Systems. An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence / MIT Press
3. [단행본] - J. F. Miller / 1997 / Designing Electronic Circuits Using Evolutionary Algorithms. Arithmetic Circuits: A Case Study / John Wiley & Son
4. [학술지(정기간행물)] - Ram Vemuri / 1994 / Using Genetic Algorithm for Constraint-Directed Design of Digital Logic Circuits / Electronics Letters
5. [학위논문] - Sushil J. Louis / 1993 / Genetic Algorithms as a Computational Tool for Design / 박사 / Indiana University
2. [단행본] - Holland, J. H. / 1992 / Adaptation in Natural and Artificial Systems. An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence / MIT Press
3. [단행본] - J. F. Miller / 1997 / Designing Electronic Circuits Using Evolutionary Algorithms. Arithmetic Circuits: A Case Study / John Wiley & Son
4. [학술지(정기간행물)] - Ram Vemuri / 1994 / Using Genetic Algorithm for Constraint-Directed Design of Digital Logic Circuits / Electronics Letters
5. [학위논문] - Sushil J. Louis / 1993 / Genetic Algorithms as a Computational Tool for Design / 박사 / Indiana University
함께 읽어보면 좋을 논문
논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!
-
A New Approach for Circuit Design Optimization using Genetic Algorithm
대한전자공학회 ISOCC
2008 .11
-
Evolutionary designing neural networks structures using genetic algorithm
제어로봇시스템학회 국제학술대회 논문집
2001 .10
-
Optimization Using Evolutionary Computation
Proceedings of The International Workshop on Future Technology
2019 .01
-
Introduction to Evolutionary Algorithms
Industrial Engineering & Management Systems
2010 .12
-
Co-Evolutionary Algorithms for Solving Multi-objective Optimization Problem
제어로봇시스템학회 국제학술대회 논문집
2002 .10
-
A New Selection Algorithms for Distributed Evolutionary Algorithms
제어로봇시스템학회 국내학술대회 논문집
2000 .10
-
A Novel Placement Method Using Evolutionary Algorithm
ITC-CSCC :International Technical Conference on Circuits Systems, Computers and Communications
1998 .01
-
On the Evolutionary Network Coding Algorithms
한국통신학회 학술대회논문집
2008 .07
-
Co-Evolutionary Algorithm for the Intelligent System
대한전자공학회 학술대회
1999 .06
-
Accelerated Co-evolutionary Algorithms
International Journal of Aeronautical and Space Sciences
2002 .05
-
Species Adaptation Evolutionary Algorithm for Solving the Optimization Problems
INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS
2003 .12
-
다이렉트사이클릭그래프에 기초한 디지털논리시스템 설계
한국인터넷방송통신학회 논문지
2009 .01
-
유전자 알고리즘에 의한 평면 및 입체 트러스의 형상 및 위상최적설계
한국공간구조학회지
2002 .01
-
Comparison of Three Evolutionary Algorithms : GA, PSO, and DE
Industrial Engineering & Management Systems
2012 .09
-
Evolutionary Algorithm을 이용한 비선형 동적 시스템의 온라인 제어 ( On-line Control of Nonlinear Dynamic System using Evolutionary Algorithm )
대한전자공학회 학술대회
1995 .07
-
Improvement of Evolutionary Computation of Genetic Algorithm using SVM
제어로봇시스템학회 국제학술대회 논문집
2003 .10
-
An Evolutionary Genetic Algorithm for the Generation of Input Pairs that Cause the Maximum Number of Switching Gates in CMOS Combinational Circuits
ITC-CSCC :International Technical Conference on Circuits Systems, Computers and Communications
2007 .07
-
Simultaneously evolutionary optimization of several natural frequencies of a two dimensional structure
Structural engineering and mechanics : An international journal
1999 .01
-
이동 로봇 행위의 실시간 진화
대한기계학회 춘추학술대회
2003 .04
-
The Co-Evolutionary Algorithms and Intelligent Systems
한국지능시스템학회 학술발표 논문집
1998 .11
이 논문의 저자 정보
최근 본 자료
댓글(0)
0
UCI(KEPA) : I410-ECN-0101-2010-560-001638311