• Title/Summary/Keyword: genetic evolutionary algorithm

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Reproduction of vibration patterns of elastic structures by block-wise modal expansion method (BMEM)

  • Jung, B.K.;Cho, J.R.;Jeong, W.B.
    • Smart Structures and Systems
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    • v.18 no.4
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    • pp.819-837
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    • 2016
  • The quality of vibration pattern reproduction of elastic structures by the modal expansion method is influenced by the modal expansion method and the sensor placement as well as the accuracy of measured natural modes and the total number of vibration sensors. In this context, this paper presents an improved numerical method for reproducing the vibration patterns by introducing a block-wise modal expansion method (BMEM), together with the genetic algorithm (GA). For a given number of vibration sensors, the sensor positions are determined by an evolutionary optimization using GA and the modal assurance criterion (MAC). Meanwhile, for the proposed block-wise modal expansion, a whole frequency range of interest is divided into several overlapped frequency blocks and the vibration field reproduction is made block by block with different natural modes and different modal participation factors. A hollow cylindrical tank is taken to illustrate the proposed improved modal expansion method. Through the numerical experiments, the proposed method is compared with several conventional methods to justify that the proposed method provides the improved results.

Weighted sum multi-objective optimization of skew composite laminates

  • Kalita, Kanak;Ragavendran, Uvaraja;Ramachandran, Manickam;Bhoi, Akash Kumar
    • Structural Engineering and Mechanics
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    • v.69 no.1
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    • pp.21-31
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    • 2019
  • Optimizing composite structures to exploit their maximum potential is a realistic application with promising returns. In this research, simultaneous maximization of the fundamental frequency and frequency separation between the first two modes by optimizing the fiber angles is considered. A high-fidelity design optimization methodology is developed by combining the high-accuracy of finite element method with iterative improvement capability of metaheuristic algorithms. Three powerful nature-inspired optimization algorithms viz. a genetic algorithm (GA), a particle swarm optimization (PSO) variant and a cuckoo search (CS) variant are used. Advanced memetic features are incorporated in the PSO and CS to form their respective variants-RPSOLC (repulsive particle swarm optimization with local search and chaotic perturbation) and CHP (co-evolutionary host-parasite). A comprehensive set of benchmark solutions on several new problems are reported. Statistical tests and comprehensive assessment of the predicted results show CHP comprehensively outperforms RPSOLC and GA, while RPSOLC has a little superiority over GA. Extensive simulations show that the on repeated trials of the same experiment, CHP has very low variability. About 50% fewer variations are seen in RPSOLC as compared to GA on repeated trials.

An Improved MAP-Elites Algorithm via Rotational Invariant Operator in Differential Evolution for Continuous Optimization (연속 최적화를 위한 개선된 MAP-Elites 알고리즘)

  • Tae Jong Choi
    • Smart Media Journal
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    • v.13 no.2
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    • pp.129-135
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    • 2024
  • In this paper, we propose a new approach that enhances the continuous optimization performance of the MAP-Elites algorithm. The existing self-referencing MAP-Elites algorithm employed the "DE/rand/1/bin" operator from the differential evolution algorithm, which, due to its lack of rotational invariance, led to a degradation in optimization performance when there were high correlations among variables. The proposed algorithm replaces the "DE/rand/1/bin" operator with the "DE/current-to-rand/1" operator. This operator, possessing rotational invariance, ensures robust performance even in cases where there are high correlations among variables. Experimental results confirm that the proposed algorithm performs better than the comparison algorithms.

Non-Identical Parallel Machine Scheduling with Sequence and Machine Dependent Setup Times Using Meta-Heuristic Algorithms

  • Joo, Cheol-Min;Kim, Byung-Soo
    • Industrial Engineering and Management Systems
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    • v.11 no.1
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    • pp.114-122
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    • 2012
  • This paper considers a non-identical parallel machine scheduling problem with sequence and machine dependent setup times. The objective of this problem is to determine the allocation of jobs and the scheduling of each machine to minimize makespan. A mathematical model for optimal solution is derived. An in-depth analysis of the model shows that it is very complicated and difficult to obtain optimal solutions as the problem size becomes large. Therefore, two meta-heuristics, genetic algorithm (GA) and a new population-based evolutionary meta-heuristic called self-evolution algorithm (SEA), are proposed. The performances of the meta-heuristic algorithms are evaluated through compare with optimal solutions using randomly generated several examples.

A Structural Learning of MLP Classifiers Using PfSGA and Its Application to Sign Language Recognition (PfSGA를 이용한 MLP분류기의 구조 학습 및 수화인식에의 응용)

  • 김상운;신성효
    • Journal of the Korean Institute of Telematics and Electronics C
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    • v.36C no.11
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    • pp.75-83
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    • 1999
  • We propose a PfSGA(parameter-free species genetic algorithm) to learn the topological structure of MLP classifiers being adequate to given applications. The PfSGA is a combinational method of SGA(species genetic algorithm) and PfGA(parameter-free genetic algorithm). In SGA, we divide the total search space into several subspaces(species) according to the number of hidden units, and reduce the unnecessary search by eliminating the low promising species from the evolutionary process. However the performances of SGA classifiers are readily affected by the values of parameters such as mutation ratio and crossover ratio. In this paper, therefore, we combine SGA with PfGA, for which it is not necessary to determine the learning parameters. Experimental results on benchmark data and sign language words show that PfSGA can reduce the learning time of SGA and is not affected by the selection parameter values on structural learning. The results also show that PfSGA is more efficient than the exisiting methods in the aspect of misclassification ratio, learning rate, and complexity of MLP structure.

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DNA Sequence Design using $\varepsilon$ -Multiobjective Evolutionary Algorithm ($\varepsilon$-다중목적함수 진화 알고리즘을 이용한 DNA 서열 디자인)

  • Shin Soo-Yong;Lee In-Hee;Zhang Byoung-Tak
    • Journal of KIISE:Software and Applications
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    • v.32 no.12
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    • pp.1217-1228
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    • 2005
  • Recently, since DNA computing has been widely studied for various applications, DNA sequence design which is the most basic and important step for DNA computing has been highlighted. In previous works, DNA sequence design has been formulated as a multi-objective optimization task, and solved by elitist non-dominated sorting genetic algorithm (NSGA-II). However, NSGA-II needed lots of computational time. Therefore, we use an $\varepsilon$- multiobjective evolutionarv algorithm ($\varepsilon$-MOEA) to overcome the drawbacks of NSGA-II in this paper. To compare the performance of two algorithms in detail, we apply both algorithms to the DTLZ2 benchmark function. $\varepsilon$-MOEA outperformed NSGA-II in both convergence and diversity, $70\%$ and $73\%$ respectively. Especially, $\varepsilon$-MOEA finds optimal solutions using small computational time. Based on these results, we redesign the DNA sequences generated by the previous DNA sequence design tools and the DNA sequences for the 7-travelling salesman problem (TSP). The experimental results show that $\varepsilon$-MOEA outperforms the most cases. Especially, for 7-TSP, $\varepsilon$-MOEA achieves the comparative results two tines faster while finding $22\%$ improved diversity and $92\%$ improved convergence in final solutions using the same time.

An Effective Method for Generating Images Using Genetic Algorithm (유전자 알고리즘을 이용한 효과적인 영상 생성 기법)

  • Cha, Joo Hyoung;Woo, Young Woon;Lee, Imgeun
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.23 no.8
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    • pp.896-902
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    • 2019
  • In this paper, we proposed two methods to automatically generate color images similar to existing images using genetic algorithms. Experiments were performed on two different sizes($256{\times}256$, $512{\times}512$) of gray and color images using each of the proposed methods. Experimental results show that there are significant differences in the evolutionary performance of each technique in genetic modeling for image generation. In the results, evolving the whole image into sub-images evolves much more effective than modeling and evolving it into a single gene, and the generated images are much more sophisticated. Therefore, we could find that gene modeling, selection method, crossover method and mutation rate, should be carefully decided in order to generate an image similar to the existing image in the future, or to learn quickly and naturally to generate an image synthesized from different images.

Developing a new mutation operator to solve the RC deep beam problems by aid of genetic algorithm

  • Kaya, Mustafa
    • Computers and Concrete
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    • v.22 no.5
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    • pp.493-500
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    • 2018
  • Due to the fact that the ratio of their height to their openings is very large compared to normal beams, there are difficulties in the design and analysis of deep beams, which differ in behavior. In this study, the optimum horizontal and vertical reinforcement diameters of 5 different beams were determined by using genetic algorithms (GA) due to the openness/height ratio (L/h), loading condition and the presence of spaces in the body. In this study, the effect of different mutation operators and improved double times sensitive mutation (DTM) operator on GA's performance was investigated. In the study following random mutation (RM), boundary mutation (BM), non-uniform random mutation (NRM), Makinen, Periaux and Toivanen (MPT) mutation, power mutation (PM), polynomial mutation (PNM), and developed DTM mutation operators were applied to five deep beam problems were used to determine the minimum reinforcement diameter. The fitness values obtained using developed DTM mutation operator was higher than obtained from existing mutation operators. Moreover; obtained reinforcement weight of the deep beams using the developed DTM mutation operator lower than obtained from the existing mutation operators. As a result of the analyzes, the highest fitness value was obtained from the applied double times sensitive mutation (DTM) operator. In addition, it was found that this study, which was carried out using GAs, contributed to the solution of the problems experienced in the design of deep beams.

A Game Level Design Technique Using the Genetic Algorithms (유전자 알고리즘을 사용한 게임 레벨 디자인 기법)

  • Kang, Shin-Jin;Shin, Seung-Ho;Cho, Sung-Hyun
    • Journal of the Korea Computer Graphics Society
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    • v.15 no.4
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    • pp.13-21
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    • 2009
  • Game level design is one of the important parts in the commercial game development. Because of its complexity in combining game components, game design work could be classified into a non-linear problem. In this paper, we propose a new automated game level design system by using genetic algorithms. With our system, a game designer easily generates an optimized game level by designating the key parameters m the initial stage of game design. Our system can be useful in reducing the trial-errors in the initial game level design process.

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Implementing Linear Models in Genetic Programming to Utilize Accumulated Data in Shipbuilding (조선분야의 축적된 데이터 활용을 위한 유전적프로그래밍에서의 선형(Linear) 모델 개발)

  • Lee, Kyung-Ho;Yeun, Yun-Seog;Yang, Young-Soon
    • Journal of the Society of Naval Architects of Korea
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    • v.42 no.5 s.143
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    • pp.534-541
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    • 2005
  • Until now, Korean shipyards have accumulated a great amount of data. But they do not have appropriate tools to utilize the data in practical works. Engineering data contains experts' experience and know-how in its own. It is very useful to extract knowledge or information from the accumulated existing data by using data mining technique This paper treats an evolutionary computation based on genetic programming (GP), which can be one of the components to realize data mining. The paper deals with linear models of GP for the regression or approximation problem when given learning samples are not sufficient. The linear model, which is a function of unknown parameters, is built through extracting all possible base functions from the standard GP tree by utilizing the symbolic processing algorithm. In addition to a standard linear model consisting of mathematic functions, one variant form of a linear model, which can be built using low order Taylor series and can be converted into the standard form of a polynomial, is considered in this paper. The suggested model can be utilized as a designing tool to predict design parameters with small accumulated data.