• Title/Summary/Keyword: Genetic Approach

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A Distributed Stock Cutting using Mean Field Annealing and Genetic Algorithm

  • Hong, Chul-Eui
    • Journal of information and communication convergence engineering
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    • v.8 no.1
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    • pp.13-18
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    • 2010
  • The composite stock cutting problem is defined as allocating rectangular and irregular patterns onto a large composite stock sheet of finite dimensions in such a way that the resulting scrap will be minimized. In this paper, we introduce a novel approach to hybrid optimization algorithm called MGA in MPI (Message Passing Interface) environments. The proposed MGA combines the benefit of rapid convergence property of Mean Field Annealing and the effective genetic operations. This paper also proposes the efficient data structures for pattern related information.

Traveling Salesman Problem with Precedence Relations based on Genetic Algorithm (선후행 관계제약을 갖는 TSP 문제의 유전알고리즘 해법)

  • Moon, Chi-Ung;Kim, Gyu-Ung;Kim, Jong-Su;Heo, Seon
    • Proceedings of the Korean Operations and Management Science Society Conference
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    • 2000.04a
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    • pp.48-51
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    • 2000
  • The traveling salesman problem with precedence relations (TSPPR) is harder than general traveling salesman problem. In this paper we propose an efficient genetic algorithm (GA) to solve the TSPPR. The key concept of the proposed genetic algorithm is a topological sort (TS). The results of numerical experiments show that the proposed GA approach produces an optimal solution for the TSPPR.

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Application of genetic algorithm to hybrid fuzzy inference engine (유전 알고리즘에 의한 Hybrid 퍼지 추론기의 구성)

  • 박세희;조현찬;이홍기;전홍태
    • 제어로봇시스템학회:학술대회논문집
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    • 1992.10a
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    • pp.863-868
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    • 1992
  • This paper presents a method on applying Genetic Algorithm(GA), which is a well-known high performance optimizing algorithm, to construct the self-organizing fuzzy logic controller. Fuzzy logic controller considered in this paper utilizes Sugeno's hybrid inference method, which has an advantage of simple defuzzification process in the inference engine. Genetic algorithm is used to find the optimal parameters in the FLC. The proposed approach will be demonstrated using 2 d.o.f robot manipulator to verify its effectiveness.

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Path planning of Autonomous Mobile robot based on a Genetic Algorithm (유전 알고리즘을 이용한 자율 이동로봇의 최적경로 계획)

  • 이동하
    • Proceedings of the Korean Society of Machine Tool Engineers Conference
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    • 2000.04a
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    • pp.147-152
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    • 2000
  • In this paper we propose a Genetic Algorithm for the path planning of an autonomous mobile robot. Genetic Algorithms(GAs) have advantages of the adaptivity such as GAs work even if an environment is time-varying or unknown. Therefore, we propose the path planning algorithms using the GAs-based approach and show more adaptive and optimal performance by simulation.

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A Hybridization of Adaptive Genetic Algorithm and Particle Swarm Optimization for Numerical Optimization Functions

  • Yun, Young-Su;Gen, Mitsuo
    • Proceedings of the Korea Society for Industrial Systems Conference
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    • 2008.10b
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    • pp.463-467
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    • 2008
  • Heuristic optimization using hybrid algorithms have provided a robust and efficient approach for solving many optimization problems. In this paper, a new hybrid algorithm using adaptive genetic algorithm (aGA) and particle swarm optimization (PSO) is proposed. The proposed hybrid algorithm is applied to solve numerical optimization functions. The results are compared with those of GA and other conventional PSOs. Finally, the proposed hybrid algorithm outperforms others.

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Integration of process planning and scheduling using simulation based genetic algorithms

  • Min, Sung-Han;Lee, Hong-Chul
    • Proceedings of the Korea Society for Simulation Conference
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    • 1998.10a
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    • pp.199-203
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    • 1998
  • Process planning and scheduling are traditionally regarded as separate tasks performed sequentially. But if two tasks are performed concurrently, greater performance can be achieved. In this study, we propose new approach to integration of process planning and scheduling. We propose new process planning combinations selection method using simulation based genetic algorithms. Computational experiments show that proposed method yield better performance when compared with existing methods.

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Fuzzy Logic Controller Design via Genetic Algorithm

  • Kwon, Oh-Kook;Wook Chang;Joo, Young-Hoon;Park, Jin-Bae
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 1998.06a
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    • pp.612-618
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    • 1998
  • The success of a fuzzy logic control system solving any given problem critically depends on the architecture of th network. Various attempts have been made in optimizing its structure its structure using genetic algorithm automated designs. In a regular genetic algorithm , a difficulty exists which lies in the encoding of the problem by highly fit gene combinations of a fixed-length. This paper presents a new approach to structurally optimized designs of a fuzzy model. We use a messy genetic algorithm, whose main characteristics is the variable length of chromosomes. A messy genetic algorithms used to obtain structurally optimized fuzzy models. Structural optimization is regarded important before neural network based learning is switched into. We have applied the method to the exampled of a cart-pole balancing.

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Handling a Multi-Tasking Environment via the Dynamic Search Genetic Algorithm

  • Koh, S.P.;Aris, I.B.;Bashi, S.M.;Chong, K.H.
    • Journal of Electrical Engineering and Technology
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    • v.3 no.1
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    • pp.125-129
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    • 2008
  • A new genetic algorithm for the solution of a multi-tasking problem is presented in this paper. The approach introduces innovative genetic operation that guides the genetic algorithm more directly towards better quality of the population. A wide variety of standard genetic parameters are explored, and results allow the comparison of performance for cases both with and without the new operator. The proposed algorithm improves the convergence speed by reducing the number of generations required to identify a near-optimal solution, significantly reducing the convergence time in each instance.

A Taguchi Approach to Parameter Setting in a Genetic Algorithm for General Job Shop Scheduling Problem

  • Sun, Ji Ung
    • Industrial Engineering and Management Systems
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    • v.6 no.2
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    • pp.119-124
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    • 2007
  • The most difficult and time-intensive issue in the successful implementation of genetic algorithms is to find good parameter setting, one of the most popular subjects of current research in genetic algorithms. In this study, we present a new efficient experimental design method for parameter optimization in a genetic algorithm for general job shop scheduling problem using the Taguchi method. Four genetic parameters including the population size, the crossover rate, the mutation rate, and the stopping condition are treated as design factors. For the performance characteristic, makespan is adopted. The number of jobs, the number of operations required to be processed in each job, and the number of machines are considered as noise factors in generating various job shop environments. A robust design experiment with inner and outer orthogonal arrays is conducted by computer simulation, and the optimal parameter setting is presented which consists of a combination of the level of each design factor. The validity of the optimal parameter setting is investigated by comparing its SN ratios with those obtained by an experiment with full factorial designs.

Design of a Fuzzy Controller Using Genetic Algorithms Employing Random Signal-Based Learning (랜덤 신호 기반 학습의 유전 알고리즘을 이용한 퍼지 제어기의 설계)

  • Han, Chang-Uk;Park, Jeong-Il
    • Journal of Institute of Control, Robotics and Systems
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    • v.7 no.2
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    • pp.131-137
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    • 2001
  • Traditional genetic algorithms, though robust, are generally not the most successful optimization algorithm on only particular domian. Hybridizing a genetic algorithm with other algorithms can produce better performance than both the genetic algorithm and the other algorithms. This paper describes the application of random signal-based learning to a genetic algorithm in order to get well tuned fuzzy rules. The key of tis approach is to adjust both the width and the center of membership functions so that the tuned rule-based fuzzy controller can generate the desired performance. The effectiveness of the proposed algorithm is verified by computer simulation.

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