• 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.

• Journal of information and communication convergence engineering
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• 제6권4호
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• pp.448-453
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• 2008

This study describes design and development techniques of estimation models for process modeling. One case study is undertaken to design a model using standard gas furnace data. Neural networks (NN) and genetic programming (GP) are each employed to model the crucial relationships between input factors and output responses. In the case study, two models were generated by using 70% training data and evaluated by using 30% testing data for genetic programming and neural network modeling. The model performance was compared by using RMSE values, which were calculated based on the model outputs. The average RMSE for training and testing were 0.8925 (training) and 0.9951 (testing) for the NN model, and 0.707227 (training) and 0.673150 (testing) for the GP model, respectively. As concern the results, the NN model has a strong advantage in model training (using the all data for training), and the GP model appears to have an advantage in model testing (using the separated data for training and testing). The performance reproducibility of the GP model is good, so this approach appears suitable for modeling physical fabrication processes.

• 한국지능시스템학회:학술대회논문집
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• 한국지능시스템학회 2008년도 춘계학술대회 학술발표회 논문집
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• pp.294-298
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• 2008

이메본 논문은 GP 트리의 노드포화도를 제어함으로써 트리의 구조공간에서 효율적인 개체 분포를 유도하는 GP 진화연산자를 제안한다. 특정 영역으로의 트리 개체의 분포가 성능에 미치는 영향을 검증하고 진화과정에서 나타나는 군집내의 개체 다양성과의 관계를 분석한다. 제안된 진화연산자를 회귀다항식, 멀티플렉서, 짝수 패리티의 3가지 벤치마크 문제에 대해서 실험을 하였고, 표준 GP 연산자와 비교하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권4호
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• pp.1765-1794
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• 2019

Genetic Programming (GP) is an intelligence technique whereby computer programs are encoded as a set of genes which are evolved utilizing a Genetic Algorithm (GA). In other words, the GP employs novel optimization techniques to modify computer programs; imitating the way humans develop programs by progressively re-writing them for solving problems automatically. Trial programs are frequently altered in the search for obtaining superior solutions due to the base is GA. These are evolutionary search techniques inspired by biological evolution such as mutation, reproduction, natural selection, recombination, and survival of the fittest. The power of GAs is being represented by an advancing range of applications; vector processing, quantum computing, VLSI circuit layout, and so on. But one of the most significant uses of GAs is the automatic generation of programs. Technically, the GP solves problems automatically without having to tell the computer specifically how to process it. To meet this requirement, the GP utilizes GAs to a "population" of trial programs, traditionally encoded in memory as tree-structures. Trial programs are estimated using a "fitness function" and the suited solutions picked for re-evaluation and modification such that this sequence is replicated until a "correct" program is generated. GP has represented its power by modifying a simple program for categorizing news stories, executing optical character recognition, medical signal filters, and for target identification, etc. This paper reviews existing literature regarding the GPs and their applications in different scientific fields and aims to provide an easy understanding of various types of GPs for beginners.

• 제어로봇시스템학회논문지
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• 제4권5호
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• pp.624-629
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• 1998

Evolutionary computation techniques can solve search problems using simulated evolution based on the ‘survival of the fittest’. Recently, the genetic programming (GP) which evolves computer programs using the genetic algorithm was introduced. In this paper, the genetic programming technique is used in order to design a rule based controller consisting of condition-action rules for an unknown system. No a priori knowledge about the structure of the controller is needed. Representation of a solution, functions and terminals in GP are analyzed, and a method of constructing a fuzzy logic controller using the obtained rule based controller is described. A simulation example using a nonlinear system shows the validity and efficiency of the proposed method.

• 정보처리학회논문지B
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• 제9B권3호
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• pp.277-286
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• 2002

양자역학 섭동이론과 유전자프로그래밍(GP) 기법을 접목시킴으로써 실세계(Real-world)에서 발생하는 카오스 시계열에 대하여 수학모델을 구축, 예측하기 위한 새로운 알고리즘을 개발하였다. 시계열 분석과 양자역학 파동방정식의 해를 구하는 섭동이론과의 절차적 유사성을 논하고, 이것을 GP로 구현하는 전형적 접근방안을 제시한다. 함수집합(Function Set)으로서 직교함수(Orthogonal Functions)를 이용하고 병렬 집단을 사용하는 GP를 이용하여 원 시계열에 대한 초기 수학모델을 구하고, 원 시계열 데이터로부터 모델의 평가값을 뺀 나머지로 구성되는 잔여 시계열에 대하여 다시 GP를 적용하는 과정을 일정한 종료조건이 충족될 때가지 반복함으로써 실세계 카오스 시계열에 대한 정확성 높은 수학모델을 구축하는데 성공하였다. 타 방법론과의 비교와 향후 해결과제에 대하여도 소개한다.

• Structural Engineering and Mechanics
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• 제36권2호
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• pp.225-241
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• 2010

In this study, a hybrid search algorithm combining genetic programming with orthogonal least squares (GP/OLS) is utilized to generate prediction models for compressive strength of high performance concrete (HPC) mixes. The GP/OLS models are developed based on a comprehensive database containing 1133 experimental test results obtained from previously published papers. A multiple least squares regression (LSR) analysis is performed to benchmark the GP/OLS models. A subsequent parametric study is carried out to verify the validity of the models. The results indicate that the proposed models are effectively capable of evaluating the compressive strength of HPC mixes. The derived formulas are very simple, straightforward and provide an analysis tool accessible to practicing engineers.

• 전기학회논문지
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• 제61권11호
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• pp.1682-1688
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• 2012

This paper introduces a GP(Genetic Programming) based robust technique for temperature compensation in short-range prediction. Development of an efficient MOS(Model Output Statistics) is necessary to correct systematic errors of the model, because forecast models do not reliably determine weather conditions. Most of MOS use a linear regression to compensate a prediction model, therefore it is hard to manage an irregular nature of prediction. In order to solve the problem, a nonlinear and symbolic regression method using GP is suggested. The purpose of this study is to evaluate the accuracy of the estimation by a GP based nonlinear MOS for 3 days temperatures in Korean regions. This method is then compared to the UM model and has shown superior results. The training period of 2007-2009 summer is used, and the data of 2010 summer is adopted for verification.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제3권1호
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• pp.43-48
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• 2014

영상의 디컨벌루션은 보통 감시 시스템에서 모션 블러 (motion blur)나 초점이 맞지 않아 발생하는 블러 (out-of-focus blur) 문제를 줄이기 위해 전처리 과정에서 사용된다. 본 논문에서는 유전 프로그래밍 (Genetic Programming, GP)에 기반한 새로운 블라인드 영상 디컨벌루션 필터링 방법을 제안한다. GP 진화 과정을 통해 영상 복원을 위한 수학적 표현이 만들어지는데, 이것은 블러 영상의 특징들 사이의 관계를 최적으로 조합하여 원래 화소 값을 복원할 수 있는 추정자 함수가 된다. 이를 위해, 먼저 각 화소의 작은 이웃으로부터 특징 벡터를 만들고 추정자 함수를 학습시키는데, 이러한 GP 진화 과정을 통해 지정한 적합성 기준에 따라 유용한 정보들이 자동으로 조합된다. 개발된 함수는 훼손된 영상의 각 화소에 적용하여 원래의 화소 값을 복원하게 된다. 개발된 함수는 다양한 방법으로 훼손된 영상에 적용하여 실험하였는데, 실험 결과 제안된 방법이 기존 알고리즘에 비해 좋은 결과를 나타내는 것을 알 수 있었다.

• 전기학회논문지
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• 제64권12호
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• pp.1748-1755
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• 2015

A linear regression is widely used for prediction problem, but it is hard to manage an irregular nature of nonlinear system. Although nonlinear regression methods have been adopted, most of them are only fit to low and limited structure problem with small number of independent variables. However, real-world problem, such as weather prediction required complex nonlinear regression with large number of variables. GP(Genetic Programming) based evolutionary nonlinear regression method is an efficient approach to attach the challenging problem. This paper introduces the improvement of an GP based nonlinear regression method using ADF(Automatically Defined Function). It is believed ADFs allow the evolution of modular solutions and, consequently, improve the performance of the GP technique. The suggested ADF based GP nonlinear regression methods are compared with UM, MLR, and previous GP method for 3 days prediction of wind speed using MOS(Model Output Statistics) for partial South Korean regions. The UM and KLAPS data of 2007-2009, 2011-2013 years are used for experimentation.