• 한국해양공학회지
• /
• 제16권4호
• /
• pp.25-31
• /
• 2002

This paper is to find optimum design of plane framed structures with discrete variables. Global search algorithms for this problem are Genetic Algorithms(GAs), Simulated Annealing(SA) and Shuffled Complex Evolution(SCE), and hybrid methods (GAs-SA, GAs-SCE). GAs and SA are heuristic search algorithms and effective tools which is finding global solution for discrete optimization. In particular, GAs is known as the search method to find global optimum or near global optimum. In this paper, reinforced concrete plane frames with rectangular section and steel plane frames with W-sections are used for the design of discrete optimization. These structures are designed for stress constraints. The robust and effectiveness of Genetic Algorithms are demonstrated through several examples.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2005년도 심포지엄 논문집 정보 및 제어부문
• /
• pp.6-8
• /
• 2005

In this paper, we discuss model identification of nonlinear data using GAs-based Fuzzy Polynomial Neural Networks(GAs-FPNN). Fuzzy Polynomial Neural Networks(FPNN) is proposed model based Group Method Data Handling(GMDH) and Neural Networks(NNs). Each node of FPNN is expressed Fuzzy Polynomial Neuron(FPN). Network structure of nonlinear data is created using Genetic Algorithms(GAs) of optimal search method. Accordingly, GAs-FPNN have more inflexible than the existing models (in)from structure selecting. The proposed model select and identify its for optimal search of Genetic Algorithms that are no. of input variables, input variable numbers and consequence structures. The GAs-FPNN model is select tuning to input variable number, number of input variable and the last part structure through optimal search of Genetic Algorithms. It is shown that nonlinear data model design using Genetic Algorithms based FPNN is more usefulness and effectiveness than the existing models.

• 산업경영시스템학회지
• /
• 제20권44호
• /
• pp.93-101
• /
• 1997

This paper describes the application of Genetic Algorithms(GAs) to nonlinear constrained mixed optimization problems. Genetic Algorithms are combinatorial in nature, and therefore are computationally suitable for treating discrete and integer design variables. But, several problems that conventional GAs are ill defined are application of penalty function that can be adapted to transform a constrained optimization problem into an unconstrained one and premature convergence of solution. Thus, we developed an improved GAs to solve this problems, and two examples are given to demonstrate the effectiveness of the methodology developed in this paper.

• 한국CDE학회논문집
• /
• 제1권3호
• /
• pp.234-241
• /
• 1996

Recently, Genetic Algorithms(GAs), which consist of genetic operators named selection crossover and mutation, are widely adapted into a search procedure for structural optimization. Contrast to traditional optimal design techniques which use design sensitivity analysis results, GAs are very simple in their algorithms and there is no need of continuity of functions(or functionals) any more in GAs. So, they can be easily applicable to wide territory of design optimization problems. Also, virtue to multi-point search procedure, they have higher probability of convergence to global optimum compared with traditional techniques which take one-point search method. The introduction of basic theory on GAs, and the application examples in combination optimization of ten-member truss structure are presented in this paper.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2001년도 ICCAS
• /
• pp.106.4-106
• /
• 2001

The current research field to find near-optimum solutions explores in a small population, which is coined as Micro-Genetic Algorithms (${\mu}$GAs), with some genetic operators. Just as in the Simple-Genetic Algorithms (SGAs), the ${\mu}$GAs work with encoding population and are implemented serially. The major difference between SGAs and ${\mu}$GAs is how to make reproductive plan for more better searching strategy due to the population choice. This paper is conducted to implement ${\mu}$GAs in order to achieve fast searching for more better evolution and associated cost evaluation in global solution space. To achieve this implementation, the Air-Borne Selection (ABS) for a new reproductive plan is developed as new strategic conception for ${\mu}$GAs. In this paper, it is shown that the ${\mu}$GAs implementation reaches a near-optimal region much earlier than the SGAs implementation. The superior ...

• 한국공간구조학회논문집
• /
• 제2권3호
• /
• pp.61-70
• /
• 2002

The objective of this study is the development of a size and shape discrete optimum design algorithms, which is based on the genetic algorithms and the fuzzy theory. This algorithms can perform both size and shape optimum designs of plane and space trusses. The developed fuzzy shape-GAs (FS-GAs) was implemented in a computer program. For the optimum design, the objective function is the weight of structures and the constraints are limits on loads and serviceability. This study solves the problem by introducing the FS-GAs operators into the genetic.

• 센서학회지
• /
• 제21권5호
• /
• pp.329-338
• /
• 2012

This paper presents an effective design method for a gas identification system. The design method adopted the sequential combination between the hybrid genetic algorithms and the TSK fuzzy logic system. First, the sensor grouping method by hybrid genetic algorithms led the effective dimensional reduction as well as effective pattern analysis from a large volume of pattern dimensions. Second, the fuzzy identification sub-models allowed handling the uncertainty of the sensor data extensively. By these advantages, the proposed identification model demonstrated high accuracy rates for identifying the five different types of gases; it was confirmed throughout the experimental trials.

• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
• /
• pp.625-628
• /
• 1998

This paper presents an application of genetic algorithms(GAs) for optimal configuration of distribution network. Three problems have been used to show how genetic algorithms are modified and applied. Solutions to the problems are found by minimizing the cost function which is directly related with balancing the loads. Simulation results show that genetic algorithms are technically feasible if they are tailored to meet the needs of real problems.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2006년도 심포지엄 논문집 정보 및 제어부문
• /
• pp.270-272
• /
• 2006

In this paper, we discuss optimal design of Fuzzy Polynomial Neural Networks by means of Genetic Algorithms(GAs) using symbolic coding for non-linear data. One of the major subject of genetic algorithms is representation of chromosomes. The proposed model optimized by the means genetic algorithms which used symbolic code to represent chromosomes. The proposed gFPNN used a triangle and a Gaussian-like membership function in premise part of rules and design the consequent structure by constant and regression polynomial (linear, quadratic and modified quadratic) function between input and output variables. The performance of the proposed model is quantified through experimentation that exploits standard data already used in fuzzy modeling. These results reveal superiority of the proposed networks over the existing fuzzy and neural models.

• 한국추진공학회:학술대회논문집
• /
• 한국추진공학회 2005년도 제25회 추계학술대회논문집
• /
• pp.299-303
• /
• 2005

본 연구에서는 축척방법에 의한 구성품 성능선도의 부정확성을 개선하기 위해 실험을 통하여 획득한 데이터를 유전자 알고리즘(Genetic Algorithms)으로 압축기 성능전도를 생성하는 방법을 제안하였고, 유전자 알고리즘만 이용할 경우 압축기 성능선도 생성시 서지점들과 쵸크점들을 예측하는데 불분명한 단점이 있어 기존의 구성품 성능선도 생성에 널리 사용하는 스케일링 기법을 보완적으로 이용하여 보다 정확한 구성품 성능선도의 예측을 시도하였다.