• Title/Summary/Keyword: Genetic Algorithm

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A Greedy Genetic Algorithm for Release Planning in Software Product Lines (소프트웨어 제품라인의 출시 계획 수립을 위한 탐욕 유전자 알고리듬)

  • Yoo, Jaewook
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.36 no.3
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    • pp.17-24
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    • 2013
  • Release planning in a software product line (SPL) is to select and assign the features of the multiple software products in the SPL in sequence of releases along a specified planning horizon satisfying the numerous constraints regarding technical precedence, conflicting priorities for features, and available resources. A greedy genetic algorithm is designed to solve the problems of release planning in SPL which is formulated as a precedence-constrained multiple 0-1 knapsack problem. To be guaranteed to obtain feasible solutions after the crossover and mutation operation, a greedy-like heuristic is developed as a repair operator and reflected into the genetic algorithm. The performance of the proposed solution methodology in this research is tested using a fractional factorial experimental design as well as compared with the performance of a genetic algorithm developed for the software release planning. The comparison shows that the solution approach proposed in this research yields better result than the genetic algorithm.

Fitness Change of Mission Scheduling Algorithm Using Genetic Theory According to the Control Constants (유전 이론을 이용한 위성 임무 스케줄링 알고리즘의 제어상수에 따른 적합도 변화 연구)

  • Cho, Kyeum-Rae;Baek, Seung-Woo;Lee, Dae-Woo
    • Journal of Institute of Control, Robotics and Systems
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    • v.16 no.6
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    • pp.572-578
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    • 2010
  • In this paper, the final fitness results of the satellite mission scheduling algorithm, which is designed by using the genetic algorithm, are simulated and compared with respect to the control constants. Heuristic algorithms, including the genetic algorithm, are good to find global optima, however, we have to find the optimal control constants before its application to a problem, because the algorithm is strongly effected by the control constants. In this research, the satellite mission scheduling algorithm is simulated with different crossover probability and mutation probability, which is major control constant of the genetic algorithm.

A Study on Fuzzy Time Series Prediction Method using the Genetic Algorithm (유전자 알고리즘을 이용한 퍼지 시계열예측 방법에 관한 연구)

  • Jee, Hyun-Min;Chang, Woo-Seok;Lee, Sung-Mok;Kang, Hwan-Il
    • Proceedings of the KIEE Conference
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    • 2005.10b
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    • pp.622-624
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    • 2005
  • This paper proposes a time series prediction method for the nonllinear system using the fuzzy system and its genetic algorithm, At first, we obtain the optimal fuzzy membership function using the genetic algorithm. With the optimal fuzzy rules and its input differences, a better time prediction series system may be obtained. We obtain a good result for the time prediction of the electric load.

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Optimizing Reliable Network using Genetic Algorithm (유전자 알고리즘을 이용한 신뢰 통신망 최적화)

  • 이학종;강주락;권기호
    • Proceedings of the IEEK Conference
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    • 1999.11a
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    • pp.452-455
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    • 1999
  • Genetic algorithm is well known as the efficient algorithm which can solve a difficult problem. Network design considering reliability is NP-hard problem with cost, distance, and volume. Therefore genetic algorithm is considered as a good method for this problem. This paper suggests the reliable network which can be constructed with minimum cost using genetic algorithm and the rank method based on reliability for improving the performance. This method shows more excellent than existing method and confirms the result through simulation.

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On-Line Character Recognition using Hidden Markov Model and Genetic Algorithm (Hidden Markov Model 과 Genetic Algorithm을 이용한 온라인 문자인식에 관한 연구)

  • 홍영표;장춘서
    • Proceedings of the IEEK Conference
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    • 2000.11c
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    • pp.29-32
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    • 2000
  • HMM(Hidden Markov Model)은 시간적인 정보를 토대로 하는 수학적인 방법으로서 문자인식에 많이 사용되어지고 있다. 그런데 HMM이 적용되고자 하는 문제에서 사용되어지는 상태 수와 HMM에서 사용되어지는 parameter들은 처음에 결정되는 값들에 의해서 상당히 많은 영향을 받게 된다. 따라서 한글의 특성을 이용한 HMM의 상태 수를 결정한 후 결정되어진 각각의 HMM parameter들을 Genetic Algorithm을 이용하였다. Genetic Algorithm은 매개변수 최적화 문제에 대하여 자연의 진화 원리를 마땅한 알고리즘으로 선택, 교배, 돌연변이 연산을 이용하여 최적의 개체를 구하게 된다. 여기서는 HMM에서의 Viterbi Algorithm을 적합도 검사에 사용하였다.

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유전적 알고리듬을 이용한 최적 구조 설계

  • 김기화
    • Bulletin of the Society of Naval Architects of Korea
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    • v.31 no.1
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    • pp.34-38
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    • 1994
  • 본 연구에서는 Genetic Algorithm을 사용하여 상기의 문제를 해결하고자 한다. 특히 다목적 함수 최적화에는 한 번의 최적화 계산으로 Pareto최적해 집합이 동시에 구해지는 새로운 방법인 MOGA(Multicriteria Optimization by Genetic Algorithm)을 개발하였다. 먼저 Genetic Alorithm의 기본 특성에 대해 살펴보고, 다양한 종류의 문제를 통해 Genetic Algorithm의 유용 성을 검토하였다.

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A Novel Hybrid Intelligence Algorithm for Solving Combinatorial Optimization Problems

  • Deng, Wu;Chen, Han;Li, He
    • Journal of Computing Science and Engineering
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    • v.8 no.4
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    • pp.199-206
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    • 2014
  • The ant colony optimization (ACO) algorithm is a new heuristic algorithm that offers good robustness and searching ability. With in-depth exploration, the ACO algorithm exhibits slow convergence speed, and yields local optimization solutions. Based on analysis of the ACO algorithm and the genetic algorithm, we propose a novel hybrid genetic ant colony optimization (NHGAO) algorithm that integrates multi-population strategy, collaborative strategy, genetic strategy, and ant colony strategy, to avoid the premature phenomenon, dynamically balance the global search ability and local search ability, and accelerate the convergence speed. We select the traveling salesman problem to demonstrate the validity and feasibility of the NHGAO algorithm for solving complex optimization problems. The simulation experiment results show that the proposed NHGAO algorithm can obtain the global optimal solution, achieve self-adaptive control parameters, and avoid the phenomena of stagnation and prematurity.

Neural Network Modeling of PECVD SiN Films and Its Optimization Using Genetic Algorithms

  • Han, Seung-Soo
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.1 no.1
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    • pp.87-94
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    • 2001
  • Silicon nitride films grown by plasma-enhanced chemical vapor deposition (PECVD) are useful for a variety of applications, including anti-reflecting coatings in solar cells, passivation layers, dielectric layers in metal/insulator structures, and diffusion masks. PECVD systems are controlled by many operating variables, including RF power, pressure, gas flow rate, reactant composition, and substrate temperature. The wide variety of processing conditions, as well as the complex nature of particle dynamics within a plasma, makes tailoring SiN film properties very challenging, since it is difficult to determine the exact relationship between desired film properties and controllable deposition conditions. In this study, SiN PECVD modeling using optimized neural networks has been investigated. The deposition of SiN was characterized via a central composite experimental design, and data from this experiment was used to train and optimize feed-forward neural networks using the back-propagation algorithm. From these neural process models, the effect of deposition conditions on film properties has been studied. A recipe synthesis (optimization) procedure was then performed using the optimized neural network models to generate the necessary deposition conditions to obtain several novel film qualities including high charge density and long lifetime. This optimization procedure utilized genetic algorithms, hybrid combinations of genetic algorithm and Powells algorithm, and hybrid combinations of genetic algorithm and simplex algorithm. Recipes predicted by these techniques were verified by experiment, and the performance of each optimization method are compared. It was found that the hybrid combinations of genetic algorithm and simplex algorithm generated recipes produced films of superior quality.

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Feature Selection and Performance Analysis using Quantum-inspired Genetic Algorithm (양자 유전알고리즘을 이용한 특징 선택 및 성능 분석)

  • Heo, G.S.;Jeong, H.T.;Park, A.;Baek, S.J.
    • Smart Media Journal
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    • v.1 no.1
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    • pp.36-41
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    • 2012
  • Feature selection is the important technique of selecting a subset of relevant features for building robust pattern recognition systems. Various methods have been studied for feature selection from sequential search algorithms to stochastic algorithms. In this work, we adopted a Quantum-inspired Genetic Algorithm (QGA) which is based on the concept and principles of quantum computing such as Q-bits and superposition of state for feature selection. The performance of QGA is compared to that of the Conventional Genetic Algorithm (CGA) with respect to the classification rates and the number of selected features. The experimental result using UCI data sets shows that QGA is superior to CGA.

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Hardware Implementation of Genetic Algorithm and Its Analysis (유전알고리즘의 하드웨어 구현 및 실험과 분석)

  • Dong, Sung-Soo;Lee, Chong-Ho
    • 전자공학회논문지 IE
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    • v.46 no.2
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    • pp.7-10
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    • 2009
  • This paper presents the implementation of libraries of hardware modules for genetic algorithm using VHDL. Evolvable hardware refers to hardware that can change its architecture and behavior dynamically and autonomously by interacting with its environment. So, it is especially suited to applications where no hardware specifications can be given in advance. Evolvable hardware is based on the idea of combining reconfigurable hardware device with evolutionary computation, such as genetic algorithm. Because of parallel, no function call overhead and pipelining, a hardware genetic algorithm give speedup over a software genetic algorithm. This paper suggests the hardware genetic algorithm for evolvable embedded system chip. That includes simulation results and analysis for several fitness functions. It can be seen that our design works well for the three examples.