• Title/Summary/Keyword: Mutation robustness

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A New Evolutionary Programming Algorithm using the Learning Rule of a Neural Network for Mutation of Individuals (신경회로망의 학습 알고리듬을 이용하여 돌연변이를 수행하는 새로운 진화 프로그래밍 알고리듬)

  • 임종화;최두현;황찬식
    • Journal of the Korean Institute of Telematics and Electronics C
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    • v.36C no.3
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    • pp.58-64
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    • 1999
  • Evolutionary programming is mainly characterized by two factors; one is the selection strategy and the other the mutation rule. In this paper, a new mutation rule that is the same form of well-known backpropagation learning rule of neural networks has been presented. The proposed mutation rule adapts the best individual's value as the target value at the generation. The temporal error improves the exploration through guiding the direction of evolution and the momentum speeds up convergence. The efficiency and robustness of the proposed algorithm have been verified through benchmark test functions.

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Global Optimization Using Differential Evolution Algorithm (차분진화 알고리듬을 이용한 전역최적화)

  • Jung, Jae-Joon;Lee, Tae-Hee
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.27 no.11
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    • pp.1809-1814
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    • 2003
  • Differential evolution (DE) algorithm is presented and applied to global optimization in this research. DE suggested initially fur the solution to Chebychev polynomial fitting problem is similar to genetic algorithm(GA) including crossover, mutation and selection process. However, differential evolution algorithm is simpler than GA because it uses a vector concept in populating process. And DE turns out to be converged faster than CA, since it employs the difference information as pseudo-sensitivity In this paper, a trial vector and its control parameters of DE are examined and unconstrained optimization problems of highly nonlinear multimodal functions are demonstrated. To illustrate the efficiency of DE, convergence rates and robustness of global optimization algorithms are compared with those of simple GA.

Evolutionary Computation for the Real-Time Adaptive Learning Control(I) (실시간 적응 학습 제어를 위한 진화연산(I))

  • Chang, Sung-Ouk;Lee, Jin-Kul
    • Proceedings of the KSME Conference
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    • 2001.06b
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    • pp.724-729
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    • 2001
  • This paper discusses the composition of the theory of reinforcement learning, which is applied in real-time learning, and evolutionary strategy, which proves its the superiority in the finding of the optimal solution at the off-line learning method. The individuals are reduced in order to learn the evolutionary strategy in real-time, and new method that guarantee the convergence of evolutionary mutations are proposed. It possible to control the control object varied as time changes. As the state value of the control object is generated, applied evolutionary strategy each sampling time because the learning process of an estimation, selection, mutation in real-time. These algorithms can be applied, the people who do not have knowledge about the technical tuning of dynamic systems could design the controller or problems in which the characteristics of the system dynamics are slightly varied as time changes. In the future, studies are needed on the proof of the theory through experiments and the characteristic considerations of the robustness against the outside disturbances.

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A Heuristic Algorithm for Asymmetric Traveling Salesman Problem using Hybrid Genetic Algorithm (혼합형 유전해법을 이용한 비대칭 외판원문제의 발견적해법)

  • 김진규;윤덕균
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.18 no.33
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    • pp.111-118
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    • 1995
  • This paper suggests a hybrid genetic algorithm for asymmetric traveling salesman problem(TSP). The TSP was proved to be NP-complete, so it is difficult to find optimal solution in reasonable time. Therefore it is important to develope an algorithm satisfying robustness. The algorithm applies dynamic programming to find initial solution. The genetic operator is uniform order crossover and scramble sublist mutation. And experiment of parameterization has been performed.

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Adaptive Learning Control of Neural Network Using Real-Time Evolutionary Algorithm (실시간 진화 알고리듬을 통한 신경망의 적응 학습제어)

  • Chang, Sung-Ouk;Lee, Jin-Kul
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.26 no.6
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    • pp.1092-1098
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    • 2002
  • This paper discusses the composition of the theory of reinforcement teaming, which is applied in real-time teaming, and evolutionary strategy, which proves its the superiority in the finding of the optimal solution at the off-line teaming method. The individuals are reduced in order to team the evolutionary strategy in real-time, and new method that guarantee the convergence of evolutionary mutations are proposed. It is possible to control the control object varied as time changes. As the state value of the control object is generated, applied evolutionary strategy each sampling time because of the teaming process of an estimation, selection, mutation in real-time. These algorithms can be applied, the people who do not have knowledge about the technical tuning of dynamic systems could design the controller or problems in which the characteristics of the system dynamics are slightly varied as time changes. In the future, studies are needed on the proof of the theory through experiments and the characteristic considerations of the robustness against the outside disturbances.

Special Protection and Control Scheme for Transmission Line Overloading Elimination Based on Hybrid Differential Evolution/Electromagnetism-Like Algorithm

  • Hadi, Mahmood Khalid;Othman, Mohammad Lutfi;Wahab, Noor Izzri Abd
    • Journal of Electrical Engineering and Technology
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    • v.12 no.5
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    • pp.1729-1742
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    • 2017
  • In designing System Protection Schemes (SPSs) in power systems, protecting transmission network against extreme undesired conditions becomes a significant challenge in mitigating the transmission line overloading. This paper presents an intelligent Special Protection and Control Scheme (SPCS) using of Differential Evolution with Adaptive Mutation (DEAM) approach to obtain the optimum generation rescheduling to solve the transmission line overloading problem in system contingency conditions. DEAM algorithm employs the attraction-repulsion idea that is applied in the electromagnetism-like algorithm to support the mutation process of the conventional Differential Evolution (DE) algorithm. Different N-1 contingency conditions under base and increase load demand are considered in this paper. Simulation results have been compared with those acquired from Genetic Algorithm (GA) application. Minimum severity index has been considered as the objective function. The final results show that the presented DEAM method offers better performance than GA in terms of faster convergence and less generation fuel cost. IEEE 30-bus test system has been used to prove the effectiveness and robustness of the proposed algorithm.

A Robust DNA Watermarking in Lifting Based 1D DWT Domain (Lifting 기반 1D DWT 영역 상의 강인한 DNA 워터마킹)

  • Lee, Suk-Hwan;Kwon, Ki-Ryong;Kwon, Seong-Geun
    • Journal of the Institute of Electronics and Information Engineers
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    • v.49 no.10
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    • pp.91-101
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    • 2012
  • DNA watermarking have been interested for both the security of private genetic information or huge DNA storage information and the copyright protection of GMO. Multimedia watermarking has been mainly designed on the basis of frequency domain, such as DCT, DWT, FMT, and so on, for the robustness and invisibility. But a frequency domain watermarking for coding DNA sequence has a considerable constraint for embedding the watermark because transform and inverse transform must be performed without completely changing the amino acid sequence. This paper presents a coding sequence watermarking on lifting based DWT domain and brings up the availability of frequency domain watermarking for DNA sequence. From experimental results, we verified that the proposed scheme has the robustness to until a combination of 10% point mutations, 5% insertion and deletion mutations and also the amino preservation and the security.

Particle Swarm Assisted Genetic Algorithm for the Optimal Design of Flexbeam Sections

  • Dhadwal, Manoj Kumar;Lim, Kyu Baek;Jung, Sung Nam;Kim, Tae Joo
    • International Journal of Aeronautical and Space Sciences
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    • v.14 no.4
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    • pp.341-349
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    • 2013
  • This paper considers the optimum design of flexbeam cross-sections for a full-scale bearingless helicopter rotor, using an efficient hybrid optimization algorithm based on particle swarm optimization, and an improved genetic algorithm, with an effective constraint handling scheme for constrained nonlinear optimization. The basic operators of the genetic algorithm, of crossover and mutation, are revisited, and a new rank-based multi-parent crossover operator is utilized. The rank-based crossover operator simultaneously enhances both the local, and the global exploration. The benchmark results demonstrate remarkable improvements, in terms of efficiency and robustness, as compared to other state-of-the-art algorithms. The developed algorithm is adopted for two baseline flexbeam section designs, and optimum cross-section configurations are obtained with less function evaluations, and less computation time.

DNA chip technology

  • Lee, Sang-Yeop;Yun, Seong-Ho;Choe, Jong-Gil;Im, Geun-Bae
    • 한국생물공학회:학술대회논문집
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    • 2000.11a
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    • pp.119-122
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    • 2000
  • Biological science is being revolutionized by the availability of much sequence information from many genome project With the advanced technology at hand, main trend in biological research is rapidly changing from a structural DNA analysis to understanding cellular function of the DNA sequences. Combined with mechanics, computer, bioinformatics and other advanced technologies, DNA chip technology provides numerous applications because of its robustness, accuracy, and automation. DNA chip is expected to become an indispensable tool in fields of biology, biotechnology, drug discovery, and other application areas. DNA chip can be used for mutation and polymorphism detection, gene expression monitoring and phenotypic analysis as well. If DNA chip is used for the development of pharmaceutical products, it can considerably reduce the cost and time for the entire process of drug discovery and development, and can also contribute in developing personal drugs.

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Robust DNA Watermarking based on Coding DNA Sequence (부호 영역 DNA 시퀀스 기반 강인한 DNA 워터마킹)

  • Lee, Suk-Hwan;Kwon, Seong-Geun;Kwon, Ki-Ryong
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.49 no.2
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    • pp.123-133
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    • 2012
  • This paper discuss about DNA watermarking using coding DNA sequence (CDS) for the authentication, the privacy protection, or the prevention of illegal copy and mutation of DNA sequence and propose a DNA watermarking scheme with the mutation robustness and the animo acid preservation. The proposed scheme selects a number of codons at the regular singularity in coding regions for the embedding target and embeds the watermark for watermarked codons and original codons to be transcribed to the same amino acids. DNA base sequence is the string of 4 characters, {A,G,C,T} ({A,G,C,U} in RNA). We design the codon coding table suitable to watermarking signal processing and transform the codon sequence to integer numerical sequence by this table and re-transform this sequence to floating numerical sequence of circular angle. A codon consists of a consecutive of three bases and 64 codons are transcribed to one from 20 amino acids. We substitute the angle of selected codon to one among the angle range with the same animo acid, which is determined by the watermark bit and the angle difference of adjacent codons. From in silico experiment by using HEXA and ANG sequences, we verified that the proposed scheme is more robust to silent and missense mutations than the conventional scheme and preserve the amino acids of the watermarked codons.