• Title, Summary, Keyword: uniform crossover

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A genetic algorithm with uniform crossover using variable crossover and mutation probabilities (동적인 교차 및 동연변이 확률을 갖는 균일 교차방식 유전 알고리즘)

  • Kim, Sung-Soo;Woo, Kwang-Bang
    • Journal of Institute of Control, Robotics and Systems
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    • v.3 no.1
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    • pp.52-60
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    • 1997
  • In genetic algorithms(GA), a crossover is performed only at one or two places of a chromosome, and the fixed probabilities of crossover and mutation have been used during the entire generation. A GA with dynamic mutation is known to be superior to GAs with static mutation in performance, but so far no efficient dynamic mutation method has been presented. Accordingly in this paper, a GA is proposed to perform a uniform crossover based on the nucleotide(NU) concept, where DNA and RNA consist of NUs and also a concrete way to vary the probabilities of crossover and mutation dynamically for every generation is proposed. The efficacy of the proposed GA is demonstrated by its application to the unimodal, multimodal and nonlinear control problems, respectively. Simulation results show that in the convergence speed to the optimal value, the proposed GA was superior to existing ones, and the performance of GAs with varying probabilities of the crossover and the mutation improved as compared to GAs with fixed probabilities of the crossover and mutation. And it also shows that the NUs function as the building blocks and so the improvement of the proposed algorithm is supported by the building block hypothesis.

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Incorporating Genetic Operators into Optimizing Highway Alignments (도로선형최적화를 위한 유전자 연산자의 적용)

  • Kim, Eung-Cheol
    • Journal of Korean Society of Transportation
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    • v.22 no.2
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    • pp.43-54
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    • 2004
  • This study analyzes characteristics and applicability of genetic algorithms and genetic operators to optimize highway alignments. Genetic algorithms, one of artificial intelligence techniques, are fast and efficient search algorithms for generating, evaluation and finding optimal highway alignment alternatives. The performance of genetic algorithms as an optimal search tool highly depends on genetic operators that are designed as a problem-specific. This study adopts low mutation operators(uniform mutation operator, straight mutation operator, non-uniform mutation operator whole non-uniform mutation operator) to explore whole search spaces, and four crossover operators(simple crossover operator, two-point crossover operator, arithmetic crossover operator, heuristic crossover operator) to exploit food characteristics of the best chromosome in previous generations. A case study and a sensitivity analysis have shown that the eight problem-specific operators developed for optimizing highway alignments enhance the search performance of genetic algorithms, and find good solutions(highway alignment alternatives). It has been also found that a mixed and well-combined use of mutation and crossover operators is very important to balance between pre-matured solutions when employing more crossover operators and more computation time when adopting more mutation operators.

Design of Optimal pm Controller Using Genetic Algorithm for Load Frequency Control of Power System (전력계통의 부하주파수 제어를 위한 유전 알고리즘을 사용한 최적 PID 제어기 설계)

  • Lee, J.P.;Wang, Y.P.;Kim, S.H.;Hur, D.R.;Chong, H.H.
    • Proceedings of the KIEE Conference
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    • pp.257-260
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    • 1997
  • This paper designs the optimal PID controller for load frequency control on 2-area power system. Genetic algorithm is utilized to optimize parameters of PID controller which is applied to power system. Using two-point crossover, uniform crossover and one-point crossover, Search performance of genetic algorithm with each crossover method is considered. In case of load variation in 1-area, the dynamic characteristic of power system is considered. The simulation results show that the proposed PID controller is better control performance than PID controller using Ziegler-Nichols method.

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Evaluation of the different genetic algorithm parameters and operators for the finite element model updating problem

  • Erdogan, Yildirim Serhat;Bakir, Pelin Gundes
    • Computers and Concrete
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    • v.11 no.6
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    • pp.541-569
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    • 2013
  • There is a wide variety of existing Genetic Algorithms (GA) operators and parameters in the literature. However, there is no unique technique that shows the best performance for different classes of optimization problems. Hence, the evaluation of these operators and parameters, which influence the effectiveness of the search process, must be carried out on a problem basis. This paper presents a comparison for the influence of GA operators and parameters on the performance of the damage identification problem using the finite element model updating method (FEMU). The damage is defined as reduction in bending rigidity of the finite elements of a reinforced concrete beam. A certain damage scenario is adopted and identified using different GA operators by minimizing the differences between experimental and analytical modal parameters. In this study, different selection, crossover and mutation operators are compared with each other based on the reliability, accuracy and efficiency criteria. The exploration and exploitation capabilities of different operators are evaluated. Also a comparison is carried out for the parallel and sequential GAs with different population sizes and the effect of the multiple use of some crossover operators is investigated. The results show that the roulettewheel selection technique together with real valued encoding gives the best results. It is also apparent that the Non-uniform Mutation as well as Parent Centric Normal Crossover can be confidently used in the damage identification problem. Nevertheless the parallel GAs increases both computation speed and the efficiency of the method.

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

  • 김진규;윤덕균
    • Journal of the Society of Korea 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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Effects, of Catalyst Pore Structure on Reactivity in Simplified Reaction System

  • Rhee, Young-Woo;Son, Jae-Ek
    • Journal of Energy Engineering
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    • v.2 no.1
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    • pp.114-122
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    • 1993
  • A model describing the reaction rate and catalyst deactivation in a simplified reaction system was developed to investigate the significance of catalyst pore structure in terms of porosities, porosity ratios, and size ratios of reactants to pores. The model showed that the unimodal catalyst could give a better performance than the bimodal in certain circumstances and the crossover found in the reactivity curves resulted from a trade-off between surface area and diffusivity. Under the assumption of uniform coke buildup, the bimodal catalyst appeared to provide better resistance to deactation than unimodal catalyst.

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Sexual Reproduction Genetic Algorithms: The Effects of Multi-Selection & Diploidy on Search Performances (유성생식 유전알고리즘 : 다중선택과 이배성이 탐색성능에 미치는 영향)

  • Ryu, K.B.;Choi, Y.J.;Kim, C.E.;Lee, H.S.;Jung, C.K.
    • Proceedings of the KIEE Conference
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    • pp.1006-1010
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    • 1995
  • This paper describes Sexual Reproduction Genetic Algorithm(SRGA) for function optimization. In SRGA, each individual utilize a diploid chromosome structure. Sex cells(gametes) are produced through artificial meiosis in which crossover and mutation occur. The proposed method has two selection operators, one, individual selection which selects the individual to fertilize, and the other, gamete selection which makes zygote for offspring production. We consider the effects of multi-selection and diploidy on search performance. SRGA improves local and global search(exploitation and exploration) and show optimum tracking performance in nonstationary environments. Gray coding is incorporated to transforming the search space and Genic uniform distribution method is proposed to alleviate the problem of premature convergence.

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Reliability-based Optimization of Steel Truss Arch Bridges

  • Cheng, Jin;Jin, Hui
    • International journal of steel structures
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    • v.17 no.4
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    • pp.1417-1427
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    • 2017
  • A reliability-based optimization approach is developed and applied to minimize the weight of steel truss arch bridges subject to probabilistic (the overall probability failure of the structure) and deterministic (stress and deflection) constraints. The method intelligently integrates the genetic algorithm (GA), the finite element method and the first order reliability method. A real-coded/integer-coded method is used to realistically represent the values of the design variables. Three GA operators consisting of constraint aggregate selection procedure, arithmetic crossover, and non-uniform mutation are proposed. The finite element method (FEM) and the first order reliability method are used to compute the value of the probabilistic and deterministic constraint functions. A numerical example involving a detailed computational model of a long span steel arch bridge with a main span of 550 m is presented to demonstrate the applicability and merits of the present method. Finally, several important parameters in the present method are discussed.

A New Concept of Power Flow Analysis

  • Kim, Hyung-Chul;Samann, Nader;Shin, Dong-Geun;Ko, Byeong-Hun;Jang, Gil-Soo;Cha, Jun-Min
    • Journal of Electrical Engineering and Technology
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    • v.2 no.3
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    • pp.312-319
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    • 2007
  • The solution of the power flow is one of the most important problems in electrical power systems. These traditional methods such as Gauss-Seidel method and Newton-Raphson (NR) method have had drawbacks up to now such as initial values, abnormal operating solutions and divergences in heavy loads. In order to overcome theses problems, the power flow solution incorporating genetic algorithm (GA) is introduced in this paper. General operator of genetic algorithm, arithmetic crossover, and non-uniform mutation operator of GA are suggested to solve the power flow problem. While abnormal solution cannot be obtained by a NR method, multiple power flow solution can be obtained by a GA method. With a heavy load, both normal solution and abnormal solution can be obtained by a proposed method. In this paper, a floating number representation instead of the binary number representation is introduced for accuracy. Simulation results have been compared with traditional methods.

Discrete optimal sizing of truss using adaptive directional differential evolution

  • Pham, Anh H.
    • Advances in Computational Design
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    • v.1 no.3
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    • pp.275-296
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    • 2016
  • This article presents an adaptive directional differential evolution (ADDE) algorithm and its application in solving discrete sizing truss optimization problems. The algorithm is featured by a new self-adaptation approach and a simple directional strategy. In the adaptation approach, the mutation operator is adjusted in accordance with the change of population diversity, which can well balance between global exploration and local exploitation as well as locate the promising solutions. The directional strategy is based on the order relation between two difference solutions chosen for mutation and can bias the search direction for increasing the possibility of finding improved solutions. In addition, a new scaling factor is introduced as a vector of uniform random variables to maintain the diversity without crossover operation. Numerical results show that the optimal solutions of ADDE are as good as or better than those from some modern metaheuristics in the literature, while ADDE often uses fewer structural analyses.