• Computers and Concrete
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• v.23 no.4
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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.

• Computers and Concrete
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• v.11 no.6
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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.

• Korean Journal of Computational Design and Engineering
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• v.3 no.1
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• pp.57-67
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• 1998

Genetic programming (GP) is an extension of a genetic algoriths paradigm, deals with tree structures representing computer programs as individuals. In recent, there have been many research activities on applications of GP to various engineering problems including system identification, data mining, function approximation, and so forth. However, standard GP suffers from the lack of the estimation techniques for numerical parameters of the GP tree that is an essential element in treating various engineering applications involving real-valued function approximations. Unlike the other research activities, where nonlinear optimization methods are employed, I adopt the use of a weighted linear associative memory for estimation of these parameters under GP algorithm. This approach can significantly reduce computational cost while the reasonable accurate value for parameters can be obtained. Due to the fact that the GP algorithm is likely to fall into a local minimum, the GP algorithm often fails to generate the tree with the desired accuracy. This motivates to devise a group of additive genetic programming trees (GAGPT) which consists of a primary tree and a set of auxiliary trees. The output of the GAGPT is the summation of outputs of the primary tree and all auxiliary trees. The addition of auxiliary trees makes it possible to improve both the teaming and generalization capability of the GAGPT, since the auxiliary tree evolves toward refining the quality of the GAGPT by optimizing its fitness function. The effectiveness of this approach is verified by applying the GAGPT to the estimation of the principal dimensions of bulk cargo ships and engine torque of the passenger car.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.31 no.4
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• pp.21-28
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• 2008

본 연구는 반도체 제조공정에서 사용되는 단일 Burn-In oven에서의 Total weighted earliness와 Tardiness를 최소화하기 위한 생산 스케줄링을 결정하는 문제를 다룬다. 본 연구에서는 모든 작업은 상시에 시작가능하고 각각은 서로 다른 가중치를 가지고 있다고 가정하였다. 일반적으로 단일 Burn-In oven은 다양한 작업들이 동시에 가능한 Batch processing 기계이다. 따라서 다양한 작업들로 구성된 하나의 Batch의 Processing time은 그 Batch 내에 있는 가장 긴 Processing time을 가지는 작업에 의해 결정된다. 본 연구에서 Batch size는 미리 결정되지 않은 상황이라고 가정한 후, 최적의 Batch 개수와 작업의 순서를 결정하기 위해 유전자 알고리즘을 적용하였다. 수리적 예제를 통해서 다양한 접근방법의 성능들을 비교한 결과, 유전자 알고리즘이 Total weighted earliness와 Tardiness를 최소화하는데 가장 뛰어난 성능을 가지고 있음을 알 수 있다.

• Journal of Intelligence and Information Systems
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• v.7 no.2
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• pp.117-127
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• 2001

The Main object of this research is directed to LG Oil company harbor in kwangyang-hang, where various ships ranging from 300 ton to 48000ton DWT use seven berths in the harbor. This harbor suffered from inefficient and unsafe management procedures since it is difficult to set guidelines for arrival and departure of ships according to the weather conditions and the current guidelines dose not offer clear basis of its implications. Therefore, it has long been suggested that these guidelines should be improved. This paper proposes a decision-support system, which can quantitatively decide the possibility of entry or departure on a harbor by analyzing the weather conditions (wind, current, and wave) and taking account of factors such as harbor characteristics, ship characteristics, weight condition, and operator characteristics. This system has been verified using 5$_{th}$ and 7$_{th}$ berth in Kwangyang-hang harbor. Machine learning technique using genetic programming(GP) is introduced to the system to quantitatively decide and produce results about the possibility of entry or arrival, and weighted linear associative memory (WLAM) method is also used to reduce the amount of calculation the GP has to perform. Group of additive genetic programming trees (GAGPT) is also used to improve learning performance by making it easy to find global optimum.mum.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.06a
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• pp.383-389
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• 2001

된 연구에서 대상으로 하고 있는 LG 정유 광양항 제품부두는 7 선석(Berth)에 재화중량(DWT) 300톤에서 48000 톤의 선박까지 다양한 선박이 이용하고 있으며, 해상의 기상상태에 따른 선박 입출향 통제 지침 설정이 어렵고, 현재 사용하고 있는 지침의 근거가 명확하지 않아 현재의 부두 운영이 비효율적이거나 안전성이 결여되어 있다고 할 수 있다. 따라서 이를 개선하기 위한 합리적인 부두운영 제한조건 개발이 절실히 요구되었다. 본 논문에서는 대상 부두의 특성, 대상 선박의 특성, 하중상태, 선박 운항자의 특성 등을 고려하여 해상/기상 상황(바람, 조류 및 파랑)에 따른 부두 입출항 가능 여부를 정량적으로 판단하고, 안전성 향상 방안을 제시할 수 있는 의사결정 시스템을 개발하고 5번, 7번 선석을 대상으로 이를 검증하였다. 여기서는 입출항 여부를 정량적으로 판단하여 결과를 제시하기 위해서 유전적 프로그래밍(Genetic Programming)을 이용한 기계학습 방법을 이용하였으며, GP의 방대한 계산량을 줄이기 위한 가중 선형 연상 기억(Weighted Linear Associative Memory: WLAM) 방법의 도입 및 전역 최적점을 쉽게 찾기 위한 Group of Additive Genetic Programming Trees(GAGPT)를 도입함으로써 학습 성능을 개선하였다.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.5
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• pp.234-240
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• 2001

Audible noise (AN) produced by corona discharges from high voltage transmission lines is one of the more important considerations in line design. Therefore, line designers must pre-determine the AN using prediction formulas. This paper presents the results of applying evolutionary computation techniques using AN data from lines throughout the world to develop new, highly accurate formulas for predicting a A-weighted AN during heavy rain and stable rain from overhead ac lines. Calculated ANs using these new formulas and existing formulas are compared with measured data.

• IE interfaces
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• v.20 no.2
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• pp.103-111
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• 2007

A heterogeneous VRP which considers various capacities, fixed and variable costs was suggested in this study. The transportation cost for vehicle is composed of its fixed and variable costs incurred proportionately to the travel distance. The main objective is to minimize the total sum of transportation costs. A mathematical programming model was suggested for this purpose and it gives an optimal solution by using OPL-STUDIO (ILOG CPLEX). A genetic algorithm which considers improvement of an initial solution, new fitness function with weighted cost and distance rates, and flexible mutation rate for escaping local solution was also suggested. The suggested algorithm was compared with the results of a tabu search and sweeping method by Taillard and Lee, respectively. The suggested algorithm gives better solutions rather than existing algorithms.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.3C
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• pp.314-322
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• 2007

This paper proposes a new optimization algorithm named by GBNSGA(Goal-Pareto Based Non-dominated Sorting Genetic Algorithm) which determines the best configuration for CR(Cognitive Radio) communication systems. Conventionally, in order to select the proper radio configuration, genetic algorithm has been introduced so as to alleviate computational burden along the execution of the cognition cycle proposed by Mitola. This paper proposes a novel optimization algorithm designated as GBNSGA for cognitive engine which can be described as a hybrid algorithm combining well-known Pareto-based NSGA(Non-dominated Sorting Genetic Algorithm) as well as GP(Goal Programming). By conducting computer simulations, it will be verified that the proposed method not only satisfies the user's service requirements in the form of goals. It reveals the fast optimization capability and more various solutions rather than conventional NSGA or weighted-sum approach.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.2 s.314
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• pp.108-115
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• 2007

This paper proposes a new optimization algorithm prescribed by GBNSGA(Goal-Pareto Based Non-dominated Sorting Genetic Algorithm) whose result satisfies the user's needs and goals to enhance the performance of optimization. Typically, lots of real-world engineering problems encounter simultaneous optimization subject to satisfying prescribed multiple objectives. Unfortunately, since these objectives might be mutually competitive, it is hardly to find a unique solution satisfying every objectives. Instead, many researches have been investigated in order to obtain an optimal solution with sacrificing more than one objectives. This paper introduces a novel optimization scheme named by GBNSGA obeying both goals as well as objectives as possible as it can via allocating candidated solutions on Pareto front, which enhances the performance of Pareto based optimization. The performance of the proposed GBNSGA will be compared with that of the conventional NSGA and weighted-sum approach.