• Journal of the Korean Operations Research and Management Science Society
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• v.20 no.1
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• pp.101-113
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• 1995

The past three decades have witnessed a tremendous growth is the literature on location proble. A mathematical formulation of uncapacitated plant location problem and the k-median as an integer program has proven very fruitful in the derivation of solution methods. Most of the successful algorithms for the problem are based on so-called "strong" linear programming relaxation. This is due to the fact that the strong linear programming relaxation provides a tight lower bound. In this paper we investigate the phenomenon with a structural analysis of the problem.e problem.

• Journal of the military operations research society of Korea
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• v.11 no.2
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• pp.53-63
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• 1985

This paper presents a multi-costraint optimization model for redundant system reliability. The optimization model is usually formulated as a nonlinear integer programming (NIP) problem. This paper reformulates the NIP problem into a linear integer programming (LIP) problem. Then an efficient 'Branch and Straddle' algorithm is proposed to solve the LIP problem. The efficiency of this algorithm stems from the simultaneous handling of multiple variables, unlike in ordinary branch and bound algorithms. A numerical example is given to illustrate this algorithm.

• Journal of applied mathematics & informatics
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• v.5 no.3
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• pp.701-716
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• 1998

In this paper we develop algorithms in programming lan-guage SCHEME for implementation of the main first order gradient techniques for unconstrained optimization. Implementation of the de-scent techniques which use non-optimal descent steps as well as imple-mentation of the optimal descent techniques are described. Also we investigate implementation of the global problem called optimization along a line. Developed programs are effective and simpler with re-spect to the corresponding in the procedural programming languages. Several numerical examples are reported.

• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.629-632
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• 1986

Both combinatorial and parametric optimization are used in computer-aided design(CAD). The most commonly occuring parametric optimization problems in engineering design such as design of control systems, design of electric circuits are usually expressed either as differentiable or as nondifferentiable semi-infinite programming(SIP) problems. In this paper we express algorithms for a class of computer-aided design problems arising in control systems.

• Journal of Digital Convergence
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• v.18 no.12
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• pp.481-488
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• 2020

Symbolic regression is an analysis method that directly generates a function that can explain the relationsip between dependent and independent variables for a given data in regression analysis. Genetic Programming is the leading technology of research in this field. It has the advantage of being able to directly derive a model that can be interpreted compared to other regression analysis algorithms that seek to optimize parameters from a fixed model. In this study, we propse a symbolic regression algorithm using parallel genetic programming based on a coarse grained parallel model, and apply the proposed algorithm to PMLB data to analyze the effectiveness of the algorithm.

• Journal of the Korea Society of Computer and Information
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• v.10 no.3 s.35
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• pp.19-34
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• 2005

The difficulties in implementing of image processing algorithms are a major reason for the lack of research into algorithm comparison. This fact makes an image processing research with difficult. We conclude that it is important to represent algorithms in form of reusable code. Since current image processing systems do not fulfill all requirements we must pose on reusable implementations, we propose to solve the reuse problem by applying generic programming. We define two dimensional iterators, which mediate between image processing algorithms and their underlying data structures, so that the same algorithm implementation can be applied to any number of different image formats. The elegance and efficiency of this approach is illustrated by a number of useful examples and demonstrated by porting in existing image processing algorithm IDE(Integrated Development Environment).

• Journal of Korea Society of Industrial Information Systems
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• v.10 no.4
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• pp.54-64
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• 2005

The study investigated genetic algorithms for the optimal control model of maximum height vertical jumping. The model includes forward dynamic simulations by the neural excitation-control variables. Convergence of genetic algorithms is very slow. In this paper the micro genetic algorithm(micro-GA) was used to reduce the computation time. Then a near optimal solution from micro-GA was an initial solution for VF02, which is one of well-developed and proven nonlinear programming algorithms. This approach provided the successful optimal solution for maximum-height jumping without a reasonable initial guess.

• The Journal of Korean Association of Computer Education
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• v.17 no.5
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• pp.89-98
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• 2014

Programming is excellent tool on realizing ideas. However students often complain of difficulties due to requiring the strict programming grammar and the highly thinking. Although various researches have been conducted to teach the programming easily for students, it should precede the analysis of what and why programming concept is difficult for learners. In this study, we analyzed what and why the programming concept is difficult for novice learners in basic programming education. Based on the results, we suggested: improving problem-solving skills based on accurate understanding and internalization on the programming concept, on reducing error between thought and execution results through the creation of sophisticated algorithms and on offering a variety of troubleshooting experience, establishing strategies to think freely for problem-solving process, and promoting the effectiveness of the learning through the learning procedure design.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.15-24
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• 2024

One of the most significant issues in combinatorial optimization is the classical NP-complete conundrum known as the 0/1 Knapsack Problem. This study delves deeply into the investigation of practical solutions, emphasizing two classic algorithmic paradigms, brute force, and dynamic programming, along with the metaheuristic and nature-inspired family algorithm known as the Genetic Algorithm (GA). The research begins with a thorough analysis of the dynamic programming technique, utilizing its ability to handle overlapping subproblems and an ideal substructure. We evaluate the benefits of dynamic programming in the context of the 0/1 Knapsack Problem by carefully dissecting its nuances in contrast to GA. Simultaneously, the study examines the brute force algorithm, a simple yet comprehensive method compared to Branch & Bound. This strategy entails investigating every potential combination, offering a starting point for comparison with more advanced techniques. The paper explores the computational complexity of the brute force approach, highlighting its limitations and usefulness in resolving the 0/1 Knapsack Problem in contrast to the set above of algorithms.

• Journal of applied mathematics & informatics
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• v.29 no.5_6
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• pp.1285-1293
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• 2011

We establishes the polynomial convergence of a new class of path-following methods for semidefinite linear complementarity problems (SDLCP) whose search directions belong to the class of directions introduced by Monteiro [9]. Namely, we show that the polynomial iteration-complexity bound of the well known algorithms for linear programming, namely the predictor-corrector algorithm of Mizuno and Ye, carry over to the context of SDLCP.