• Computers and Concrete
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• 제23권4호
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• pp.255-265
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• 2019

The potential of using genetic programming to predict engineering data has caught the attention of researchers in recent years. The present paper utilized weighted genetic programming (WGP), a derivative model of genetic programming (GP), to model the compressive strength of concrete. The calculation results of Abrams' laws, which are used as the design codes for calculating the compressive strength of concrete, were treated as the inputs for the genetic programming model. Therefore, knowledge of the Abrams' laws, which is not a factor of influence on common data-based learning approaches, was considered to be a potential factor affecting genetic programming models. Significant outcomes of this work include: 1) the employed design codes positively affected the prediction accuracy of modeling the compressive strength of concrete; 2) a new equation was suggested to replace the design code for predicting concrete strength; and 3) common data-based learning approaches were evolved into knowledge-based learning approaches using historical data and design codes.

• 한국CDE학회논문집
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• 제14권6호
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• pp.382-389
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• 2009

Recently, knowledge management has been required in companies as a tool of competitiveness. Companies have constructed Enterprise Resource Planning(ERP) system in order to manage huge knowledge. But, it is not easy to formalize knowledge in organization. We focused on data mining system by genetic programming(GP). Data mining system by genetic programming can be useful tools to derive and extract the necessary information and knowledge from the huge accumulated data. However when we don't have enough amounts of data to perform the learning process of genetic programming, we have to reduce input parameter(s) or increase number of learning or training data. In this study, an enhanced data mining method combining Genetic Programming with Self organizing map, that reduces the number of input parameters, is suggested. Experiment results through a prototype implementation are also discussed.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제2권1호
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• pp.20-25
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• 2002

This paper presents a genetic programming based evolutionary strategy for on-line adaptive learnable evolvable hardware. Genetic programming can be useful control method for evolvable hardware for its unique tree structured chromosome. However it is difficult to represent tree structured chromosome on hardware, and it is difficult to use crossover operator on hardware. Therefore, genetic programming is not so popular as genetic algorithms in evolvable hardware community in spite of its possible strength. We propose a chromosome representation methods and a hardware implementation method that can be helpful to this situation. Our method uses context switchable identical block structure to implement genetic tree on evolvable hardware. We composed an evolutionary strategy for evolvable hardware by combining proposed method with other's striking research results. Proposed method is applied to the autonomous mobile robots cooperation problem to verify its usefulness.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.1177-1180
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• 1996

In this paper, we propose an algorithm that realizes cooperative behavior by construction of autonomous mobile robot system. Each robot is able to sense other robots and obstacles, and it has the rule of behavior to achieve the goal of the system. In this paper, to improve performance of the whole system, we use Genetic Programming based on Natural Selection. Genetic Programming's chromosome is a program of tree structure and it's major operators are crossover and mutation. We verify the effectiveness of the proposed scheme from the several examples.

• 정보기술응용연구
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• 제3권3호
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• pp.25-40
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• 2001

본 논문에서는 유전적 프로그래밍(Genetic Programing)을 이용하여 최적의 다항식을 생성하는 기법을 제시하고자 한다. 다항식은 비선형성이 큰 응답면을 모델링해야 하며, 이를 위하여 GP 트리 생성시 2-3차 오더의 Taylor Series를 사용하는 방법을 시도하였다. 아울러 생서되는 다항식의 크기를 제어하기 위해서 GP 트리가 표현할 수 있는 다항식의 최대 차수를 제한함과 동시에 하나의 주 트리와 보 트리로 구성되는 GAGPT(Group of Additive Genetic Programming Trees) 사용을 모색하였다. 마지막으로 두 개의 응용 예를 통하여 본 방법의 유용성을 검증하였다.

• 디지털융복합연구
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• 제18권12호
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• pp.481-488
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• 2020

기호적 회귀분석 (Symbolic Regression)은 회귀분석에서 주어진 데이터에 대하여 종속변수와 독립변수들 사이의 관계를 설명할 수 있는 함수를 직접 생성하는 분석방법으로서 Genetic Programming 이 본 분야의 연구에 가장 선도적으로 적용되고 있으며, 고정된 모델로부터 매개변수들의 최적화를 추구하는 다른 회귀분석 알고리즘들에 비하여 해석이 가능한 모델을 직접 도출할 수 있다는 장점을 갖는다. 본 연구에서는 Coarse grained 병렬 모델에 기반한 Parellel Genetic Programming 을 이용한 symbolic regression 알고리즘을 제시하고 제시된 알고리즘을 PMLB 데이타에 적용하여 해당 알고리즘의 효용성을 분석하고자 한다.

• 한국지하수토양환경학회:학술대회논문집
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• 한국지하수토양환경학회 2002년도 추계학술발표회
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• pp.24-27
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• 2002

The optimal designs of groundwater problems of contaminant containment and cleanup using linear programming and genetic algorithm are provided. In the containment problem, genetic algorithm shows the superior feature to linear programming. In cleanup problem, genetic algorithm makes reasonable optimal design. Un this study, it is demonstrated through numerical experiments that genetic algorithm can be applied to remedial designs of groundwater problems.

• 제어로봇시스템학회논문지
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• 제16권4호
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• pp.339-345
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• 2010

This paper introduces GP(Genetic Programming) based robust corner detectors for scaled and rotated images. Various empirical algorithms have been studied to improve computational speed and accuracy including approaches, such as the Harris and SUSAN, FAST corner detectors. These techniques are highly efficient for well-defined corners, but are limited to corner-like edges which are often generated in rotated images. It is very difficult to detect correctly edges which have characteristics similar to corners. In this paper, we have focused the above challenging problem and proposed Genetic Programming-based automated generation of corner detectors which is robust to scaled and rotated images. The proposed method is compared to the existing corner detectors on test images and shows superior results.

• Computers and Concrete
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• 제11권6호
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• pp.603-620
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• 2013

This paper improves genetic programming (GP) and weight genetic programming (WGP) and proposes soft-computing polynomials (SCP) for accurate prediction and visible polynomials. The proposed genetic programming system (GPS) comprises GP, WGP and SCP. To represent confined compressive strength and strain of circular concrete columns in meaningful representations, this paper conducts sensitivity analysis and applies pruning techniques. Analytical results demonstrate that all proposed models perform well in achieving good accuracy and visible formulas; notably, SCP can model problems in polynomial forms. Finally, concrete compressive strength and lateral steel ratio are identified as important to both confined compressive strength and strain of circular concrete columns. By using the suggested formulas, calculations are more accurate than those of analytical models. Moreover, a formula is applied for confined compressive strength based on current data and achieves accuracy comparable to that of neural networks.

• 정보기술응용연구
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• 제3권3호
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• pp.1-24
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• 2001

본 논문은 최소한의 학습데이터를 사용하여 비선형의 응답면을 모델링할 수 있는 방안으로 유전적 프로그래밍을(Genetic Programming, GP)의 사용을 모색하였다. 이때 대두되는 가장 큰 문제는 GP 트리가 부족한 학습 데이터 때문에 심한 Overfilling 현상을 보인다는 점이다.이를 극복하기 위한 방법으로 DDBS (Directional Derivative-Based Smoothering) 기법을 제안하였고, 유용성을 검증하기 위해서 4 가지 응용 예를 보였다.