• 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.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.9-11
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• 2005

We propose a optimal identification of information granulation(IG)-based fuzzy model to carry out the model identification of complex and nonlinear systems. To optimally identity we use genetic algorithm (GAs) sand Hard C-Means (HCM) clustering. An initial structure of fuzzy model is identified by determining the number of input, the selected input variables, the number of membership function, and the conclusion inference type by means of GAs. Granulation of information data with the aid of Hard C-Means(HCM) clustering algorithm help determine the initial parameters of fuzzy model such as the initial apexes of the membership functions and the initial values of polynomial functions being used in the premise and consequence part of the fuzzy rules. And the initial parameters are tuned effectively with the aid of the genetic algorithms(GAs) and the least square method. Numerical example is included to evaluate the performance of the proposed model.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.03a
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• pp.81-84
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• 1998

Genetic Algorithms (GAs) are stochastic algorithms whose search methods model some natural phenomena. The procedure of GAs may be divided into two sub-procedures : Operation and Selection. Chromosomes can produce new offspring by means of operation, and the fitter chromosomes can produce more offspring than the less fit ones by means of selection. However, operation which is executed randomly and has some limits to its execution can not guarantee to produce fitter chromosomes. Thus, we propose a method which gives a directional information to the genetic operator by reinforcement learning. It can be achived by using neural networks to apply reinforcement learning to the genetic operator. We use the amount of fitness change which can be considered as reinforcement signal to calcualte the error terms for the output units. Then the weights are updated using backpropagtion algorithm. The performance improvement of GAs using reinforcement learning can be measured by applying the pr posed method to GA-hard problem.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.1
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• pp.46-51
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• 1997

The optimal control of a continuous process has been performed using genetic algorithms(GAs). GAs are robust and easily applicable for complex and highly nonlinear problems. We introduce the heuristics 'dynamic range' which reduces the search space dramaticaly keeping the robust search of GAs. GAs with dynamic range show the better performance than SQP(Successive Quadratic Programing) method which converges to a local minimum. The proposed methology has been applied to the optimal control of the continuous MMA-VA copolymerization reactor for the production of the desired molecular wieght and the composition of VA in dead copolymer.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2002.10a
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• pp.203-210
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• 2002

The great advantages on the Genetic Algorithms(GAs) are ease of implementation, and robustness in solving a wide variety of problems, several GAs based optimization models for solving complex structural problems were proposed. However, there are two major disadvantages in GAs. The first disadvantage, implementation of GAs-based optimization is computationally too expensive for practical use in the field of structural optimization, particularly for large-scale problems. The second problem is too difficult to find proper parameter for particular problem. Therefore, in this paper, a Distributed Hybrid Genetic Algorithms(DHGAs) is developed for structural optimization on a cluster of personal computers. The algorithm is applied to the minimum weight design of steel structures.

• Journal of Korean Association for Spatial Structures
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• v.1 no.1 s.1
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• pp.125-134
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• 2001

The objective of this study is the development of size discrete optimum design algorithm which is based on the GAs(genetic algorithms). The algorithm can perform size discrete optimum designs of space trusses. The developed algorithm was implemented in a computer program. For the optimum design, the objective function is the weight of space trusses and the constraints are limite state design codes(1998) and displacements. The basic search method for the optimum design is the GAs. The algorithm is known to be very efficient for the discrete optimization. This study solves the problem by introducing the GAs. The GAs consists of genetic process and evolutionary process. The genetic process selects the next design points based on the survivability of the current design points. The evolutionary process evaluates the survivability of the design points selected from the genetic process. In the genetic process of the simple GAs, there are three basic operators: reproduction, cross-over, and mutation operators. The efficiency and validity of the developed discrete optimum design algorithm was verified by applying GAs to optimum design examples.

• Journal of Electrical Engineering and information Science
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• v.2 no.5
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• pp.51-59
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• 1997

This paper presets a methodology of improving a conventional model in power systems using Genetic Algorithms(GAs) and suggests a GAs-based model which can directly solve the real-valued optimum in an optimization procedure. In applying GAs to the power flow, a new fitness mapping method is proposed using the proposed using the probability distribution function for all the payoffs in the population pool. In this approach, both the notions on a way of the genetic representation, and a realization of the genetic operators are fully discussed to evaluate he GAs' effectiveness. The proposed method is applied to IEEE 5-bus, 14-bus and 25-bus systems and, the results of computational experiments suggest a direct applicability of GAs to more complicated power system problems even if they contain nonlinear algebraic equations.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.1
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• pp.33-38
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• 2006

In this paper, we propose a new architecture of Fuzzy Polynomial Neural Networks(FPNN) by means of genetically optimized Fuzzy Polynomial Neuron(FPN) and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially Genetic Algorithms(GAs). The conventional FPNN developed so far are based on mechanisms of self-organization and evolutionary optimization. The design of the network exploits the extended Group Method of Data Handling(GMDH) with some essential parameters of the network being provided by the designer and kept fixed throughout the overall development process. This restriction may hamper a possibility of producing an optimal architecture of the model. The proposed FPNN gives rise to a structurally optimized network and comes with a substantial level of flexibility in comparison to the one we encounter in conventional FPNNs. It is shown that the proposed advanced genetic algorithms based Fuzzy Polynomial Neural Networks is more useful and effective than the existing models for nonlinear process. We experimented with Medical Imaging System(MIS) dataset to evaluate the performance of the proposed model.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.1
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• pp.21-28
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• 2005

This paper proposes a new method for accelerating the search speed of genetic algorithms by taking derivative evaluation and conditional random selection into account in their evolution process. Derivative evaluation makes genetic algorithms focus on the individuals whose fitness is rapidly increased. This accelerates the search speed of genetic algorithms by enhancing exploitation like steepest descent methods but also increases the possibility of a premature convergence that means most individuals after a few generations approach to local optima. On the other hand, derivative evaluation under a premature convergence helps genetic algorithms escape the local optima by enhancing exploration. If GAs fall into a premature convergence, random selection is used in order to help escaping local optimum, but its effects are not large. We experimented our method with one combinatorial problem and five complex function optimization problems. Experimental results showed that our method was superior to the simple genetic algorithm especially when the search space is large.

• Journal of the Korean Operations Research and Management Science Society
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• v.23 no.3
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• pp.1-15
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• 1998

Genetic algorithms(GAs) are suited for solving structural design problems, since they handle the design variables efficiently. This ability of GAs considers then as a good choice for optimization problems. Nevertheless, there are many situations that the conventional genetic algorithms do not perform particularly well, and so various methods of hybridization have been proposed. Thus. this paper develops a hybrid genetic algorithm(HGA) to incorporate a local convergence method and precision search method around optimum in the genetic algorithms. In case study. it is showed that HGA is able consistently to provide efficient, fine quality solutions and provide a significant capability for solving structural design problems.