• Title/Summary/Keyword: 진화기법

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A Regression Test Method for Program Evolving by Using AOP (AOP를 이용하여 진화된 프로그램의 회귀 테스트 기법)

  • Lee, Mee-Jin;Choi, Eun-Man
    • Proceedings of the Korea Information Processing Society Conference
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    • 2007.05a
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    • pp.263-266
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    • 2007
  • 이미 개발된 소프트웨어를 확장, 진화시킬 때 AOP 기법을 이용하면 모듈화뿐만 아니라 동적으로 플러그인 시킬 수 있어 편리하다. 즉 객체 지향 방식으로 개발된 프로그램에 AOP 를 적용하여 확장하면 여러 모듈에 걸쳐 나타나는 횡단 관심사를 기존 프로그램의 수정 없이 기능을 추가할 수 있다. 이미 테스트까지 마친 소프트웨어에 AOP 를 적용하여 확장한 경우 AOP 특성에 맞는 회귀 테스트 방법이 필요하다. 본 논문에서는 AOP 를 이용하여 진화된 프로그램의 회귀 테스트 방법을 제안하였으며 사례 연구에 의하여 그 효용성을 보였다.

Neural Networks based on Cellular Automata (셀룰라 오토마아에 기반한 신경망)

  • Cho, Yong-Goon;Shin, Suk-Young;Kang, Hoon
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 1998.03a
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    • pp.57-60
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    • 1998
  • Darwin Machine은 자기 자신의 구조를 전자적인 속도로 진화해 나가는 하드웨어로서 복잡한 구조와 성질으 진화 기법을 사용하여 만들어 나가는 진화공학(Evolutionary Engineering)의 한 예이다. 하드웨어가 전자적인 속도로 진화하기 위해서는 각각으리 하드웨어 구성요소들이 병렬적으로 작동해햐 하는데 셀룰라 오토마타는 이러한 문제를 해결하는 적합한 구조이며, 하드췌어에 쉽게 이식할 수 있는 장점이 있다. 신경망의 학습 능력과 진한 연산을 이용하면 효율적인 진화를 유도할 수 있다. 본 논문에서는 이러한 하드웨어 구현을 위한 셀룰라 오토마타에 기반한 신경망을 보이고자 한다.

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Structural Optimization of Planar Truss using Quantum-inspired Evolution Algorithm (양자기반 진화알고리즘을 이용한 평면 트러스의 구조최적화)

  • Shon, Su-Deok;Lee, Seung-Jae
    • Journal of the Korea institute for structural maintenance and inspection
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    • v.18 no.4
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    • pp.1-9
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    • 2014
  • With the development of quantum computer, the development of the quantum-inspired search method applying the features of quantum mechanics and its application to engineering problems have emerged as one of the most interesting research topics. This algorithm stores information by using quantum-bit superposed basically by zero and one and approaches optional values through the quantum-gate operation. In this process, it can easily keep the balance between the two features of exploration and exploitation, and continually accumulates evolutionary information. This makes it differentiated from the existing search methods and estimated as a new algorithm as well. Thus, this study is to suggest a new minimum weight design technique by applying quantum-inspired search method into structural optimization of planar truss. In its mathematical model for optimum design, cost function is minimum weight and constraint function consists of the displacement and stress. To trace the accumulative process and gathering process of evolutionary information, the examples of 10-bar planar truss and 17-bar planar truss are chosen as the numerical examples, and their results are analyzed. The result of the structural optimized design in the numerical examples shows it has better result in minimum weight design, compared to those of the other existing search methods. It is also observed that more accurate optional values can be acquired as the result by accumulating evolutionary information. Besides, terminal condition is easily caught by representing Quantum-bit in probability.

Differential Evolution Algorithm based on Random Key Representation for Traveling Salesman Problems (외판원 문제를 위한 난수 키 표현법 기반 차분 진화 알고리즘)

  • Lee, Sangwook
    • The Journal of the Korea Contents Association
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    • v.20 no.11
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    • pp.636-643
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    • 2020
  • The differential evolution algorithm is one of the meta-heuristic techniques developed to solve the real optimization problem, which is a continuous problem space. In this study, in order to use the differential evolution algorithm to solve the traveling salesman problem, which is a discontinuous problem space, a random key representation method is applied to the differential evolution algorithm. The differential evolution algorithm searches for a real space and uses the order of the indexes of the solutions sorted in ascending order as the order of city visits to find the fitness. As a result of experimentation by applying it to the benchmark traveling salesman problems which are provided in TSPLIB, it was confirmed that the proposed differential evolution algorithm based on the random key representation method has the potential to solve the traveling salesman problems.

A Study on Automatic Design of Artificial Meural Networks using Cellular Automata Techniques (샐룰라 오토마타 기법을 이용한 신경망의 자동설계에 관한 연구)

  • Lee, Dong-Wook;Sim, Kwee-Bo
    • Journal of the Korean Institute of Telematics and Electronics S
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    • v.35S no.11
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    • pp.88-95
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    • 1998
  • This paper is the result of constructing information processing system such as living creatures' brain based on artificial life techniques. The living things are best information processing system in themselves. One individual is developed from a generative cell. And a species of this individual has adapted itself to the environment through evolution. In this paper, we propose a new method of designing neural networks using biological inspired developmental and evolutionary concept. Ontogeny of organism is embodied in cellular automata(CA) and phylogeny of species is realized by evolutionary algorithms(EAs). We call 'Evolving Cellular Automata Neural Systems' as ECANSI. The connection among cells is determined by the rule of cellular automata. In order to obtain the best neural networks in given environment, we evolve the arragemetn of initial cells. The cell, that is a neuron of neural networks, is modeled on chaotic neuron with firing or rest state like biological neuron. A final output of network is measured by frequency of firing state. The effectiveness of the proposed scheme is verified by applying it to Exclusive-OR and parity problem.

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Adaptive Power Control Algorithm based on the Evolutionary Game Theory (진화게임이론을 이용한 적응적 전력제어 알고리즘)

  • Kim, Deok-Joo;Kim, Sung-Wook
    • Journal of KIISE:Information Networking
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    • v.37 no.3
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    • pp.228-233
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    • 2010
  • During wireless network operations, adaptive power control is an effective way to enhance the network performance. In this paper, a new online power control scheme is proposed based on the evolutionary game theory. To converge a desirable network equilibrium, the proposed scheme adaptively adjusts a transmit power level in a distributed online manner. With a simulation study, we demonstrate that the proposed scheme improves network performance under widely diverse network environments.

ISAR Motion Compensation using Evolutionary Programming-Based Time-Frequency Analysis (진화 프로그래밍 기반의 시간-주파수 영역 해석법을 이용한 ISAR 영상 이동보상기법)

  • 최인식;김효태
    • The Journal of Korean Institute of Electromagnetic Engineering and Science
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    • v.14 no.11
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    • pp.1156-1160
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    • 2003
  • Many time-frequency analysis techniques have been used for motion compensated ISAR(Inverse Synthetic Aperture Radar) imaging. In this work, a novel time-frequency(T-F) analysis called evolutionary adaptive wavelet transform (EAWT) and evolutionary adaptive joint time-frequency(EAJTF) procedure are used for the motion compensated ISAR image. To show the validity of our algorism, we use simulated MIG-25 and Boeing 727(B-727) ISAR data. From the constructed ISAR image using EAWT and EAJTF, we show that our algorithm can obtain a clear motion compensated ISAR image such as other time-frequency analysis techniques.

식물모사 유동 연구개발 동향

  • Lee, Jin-Gi
    • Journal of the KSME
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    • v.53 no.5
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    • pp.35-40
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    • 2013
  • 이 글에서는 최근 많은 연구가 수행되는 자연모사 기법 중 식물모사 기법을 소개하고자 한다. 식물의 유동 원리 및 중요성, 식물 유동원리를 이용한 식물모사 마이크로 디바이스의 설계, 식물의 진화혼적 최적화의 공학적 접근에 대해 논한다.

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Fast 3D Model Extraction Algorithm with an Enhanced PBIL of Preserving Depth Consistency (깊이 일관성을 보존하는 향상된 개체군기반 증가 학습을 이용한 고속 3차원 모델 추출 기법)

  • 이행석;장명호;한규필
    • Journal of KIISE:Computer Systems and Theory
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    • v.31 no.1_2
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    • pp.59-66
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    • 2004
  • In this paper, a fast 3D model extraction algorithm with an enhanced PBIL of preserving depth consistency is proposed for the extraction of 3D depth information from 2D images. Evolutionary computation algorithms are efficient search methods based on natural selection and population genetics. 2D disparity maps acquired by conventional matching algorithms do not match well with the original image profile in disparity edge regions because of the loss of fine and precise information in the regions. Therefore, in order to decrease the imprecision of disparity values and increase the quality of matching, a compact genetic algorithm is adapted for matching environments, and the adaptive window, which is controlled by the complexity of neighbor disparities in an abrupt disparity point is used. As the result, the proposed algorithm showed more correct and precise disparities were obtained than those by conventional matching methods with relaxation scheme.