• Korean Management Science Review
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• v.26 no.1
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• pp.197-208
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• 2009

In this paper, we study stochastic maximal covering location problem considering floating population. Traditional maximal covering location problem assumed that number of populations at demand point is already known and fixed. In this manner, someone who try to solve real world maximal covering location problem must consider administrative population as a population at demand point. But, after observing floating population, appliance of population in steady-state is more reasonable. In this paper, we suggest revised numerical model of maximal covering location problem. We suggest heuristic methodology to solve large scale problem by using genetic algorithm.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.5
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• pp.540-548
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• 2012

The Weapon Target Allocation(WTA) problem is the NP-Complete problem. The WTA problem is that the threatful air targets are assigned by weapon of allies for killing the targets. A good solution of NP-complete problem is heuristic algorithms. Genetic algorithms are commonly used heuristic for global optimization, and it is good solution on the diverse problem domain. But there has been very little research done on the generation of their initial population. The initialization of population is one of the GA step, and it decide to initial value of individuals. In this paper, we propose to the population initialization method to improve a Genetic Algorithm. When it initializes population, the proposed algorithm reflects the characteristics of the WTA problem domain, and inherits the dominant gene. In addition, the search space widely spread in the problem space to find efficiently the good quality solution. In this paper, the proposed algorithm to verify performance examine that an analysis of various properties and the experimental results by analyzing the performance compare to other algorithms. The proposed algorithm compared to the other initialization methods and a general genetic algorithm. As a result, the proposed algorithm showed better performance in WTA problem than the other algorithms. In particular, the proposed algorithm is a good way to apply to the variety of situation WTA problem domain, because the proposed algorithm can be applied flexibly to WTA problem by the adjustment of RMI.

• Communications of the Korean Mathematical Society
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• v.15 no.4
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• pp.605-616
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• 2000

We prove the non-homogeneous boundary value problem for population evolution equations is well-posed in Sobolev space H(sup)3/2,3/2($\Omega$). It provides a strictly mathematical basis for further research of population control problems.

• Korea journal of population studies
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• v.8 no.2
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• pp.68-78
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• 1985

It is well recognized that, in most developing countries, rapid population growth has been a serious problem. And there is a growing tendency to regard the problem as a political issue in these coun-tries. So far, many developing countries have formulated and implemented population policies aiming at an reduction of such population growth. This study attempts, in policy perspective, to examine theoretically the process and determinants of population policy making and implementation in those developing countries. In doing this, it gives emphasis on explaining population policy determinants and therefore, deals with (1) decision makers' perceptions and attitudes, (2) governmental structure and capability, (3) mass fertiliry behavior, and (4) foreign aid agencies' role.

• Journal of the Korea Safety Management & Science
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• v.11 no.4
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• pp.213-220
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• 2009

The minimum latency problem, also known as the traveling repairman problem and the deliveryman problem is to minimize the overall waiting times of customers, not to minimize their routing times. In this research, a genetic algorithm, a clonal selection algorithm and a population management genetic algorithm are introduced. The computational experiment shows the objective value of the clonal selection algorithm is the best among the three algorithms and the calculating time of the population management genetic algorithm is the best among the three algorithms.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.3
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• pp.191-199
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• 2001

This paper presents a new genetic algorithm for job-shop scheduling problems. When we design a genetic algorithm for difficult ordering problems such as job-shop scheduling problems, it is important to design encoding/crossover that is excellent in characteristic preservation and to maintain a diversity of population. We used Job-based order crossover(JOX). Since the schedules generated by JOX are not always active-schedule, we proposed a method to transform them into active schedulesby using the GT method with c)laracteristic preservation. We introduce strategies for maintaining a diversity of the population by eliminating same individuals in the population. Furthermore, we are not used mutation. Experiments have been done on two examples: Fisher s and Thompson s $lO\timeslO and 20\times5$ benchmark problem.

• Journal of the Korean Statistical Society
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• v.22 no.1
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• pp.71-81
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• 1993

Among k independent normal populations with unknown means and a common unknown variance, the problem of detecting the population with the largest absolute value of mean is considered. This problem is formulated in a manner close to the framework of testing hypotheses, and the maximum error probability and the minimum power are considered. The power charts necessary to determine the sample size are provided. The problem of detecting the population with the smallest absolute value of mean is also considered.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.220-225
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• 2021

Human population growth rate is an important parameter for real-world planning. Common approaches rely upon fixed parameters like human population, mortality rate, fertility rate, which is collected historically to determine the region's population growth rate. Literature does not provide a solution for areas with no historical knowledge. In such areas, machine learning can solve the problem, but a multitude of machine learning algorithm makes it difficult to determine the best approach. Further, the missing feature is a common real-world problem. Thus, it is essential to compare and select the machine learning techniques which provide the best and most robust in the presence of missing features. This study compares 17 machine learning techniques (base learners and ensemble learners) performance in predicting the human population growth rate of the country. Among the 17 machine learning techniques, random forest outperformed all the other techniques both in predictive performance and robustness towards missing features. Thus, the study successfully demonstrates and compares machine learning techniques to predict the human population growth rate in settings where historical data and feature information is not available. Further, the study provides the best machine learning algorithm for performing population growth rate prediction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.335-352
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• 2023

When solving multi-objective optimization problems, the blindness of the evolution direction of the population gradually emerges with the increase in the number of objectives, and there are also problems of convergence and diversity that are difficult to balance. The many- objective optimization problem makes some classic multi-objective optimization algorithms face challenges due to the huge objective space. The sine cosine algorithm is a new type of natural simulation optimization algorithm, which uses the sine and cosine mathematical model to solve the optimization problem. In this paper, a knee-driven many-objective sine-cosine algorithm (MaSCA-KD) is proposed. First, the Latin hypercube population initialization strategy is used to generate the initial population, in order to ensure that the population is evenly distributed in the decision space. Secondly, special points in the population, such as nadir point and knee points, are adopted to increase selection pressure and guide population evolution. In the process of environmental selection, the diversity of the population is promoted through diversity criteria. Through the above strategies, the balance of population convergence and diversity is achieved. Experimental research on the WFG series of benchmark problems shows that the MaSCA-KD algorithm has a certain degree of competitiveness compared with the existing algorithms. The algorithm has good performance and can be used as an alternative tool for many-objective optimization problems.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.6
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• pp.2269-2275
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• 2010

The performance of Genetic Algorithms (GA) is affected by various factors such as parameters, genetic operators and strategies. The traditional approach with random initial population is efficient however the whole initial population may contain many infeasible solutions. Thus it would take a long time for GA to produce a good solution. The GA have been modified in various ways to achieve faster convergence and it was particularly recognized by researchers that initial population greatly affects the performance of GA. This study proposes modified GA with sorted initial population and applies it to solving Travelling Salesman Problem (TSP). Normally, the bigger the initial the population is the more computationally expensive the calculation becomes with each generation. New approach allows reducing the size of the initial problem and thus achieve faster convergence. The proposed approach is tested on a simulator built using object-oriented approach and the test results prove the validity of the proposed method.