• The KIPS Transactions:PartB
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• v.14B no.7
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• pp.541-548
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• 2007

Due to the mathematical convergence property, Simulated Annealing (SA) has been one of the most popular optimization algorithms. However, because of its problem of slow convergence in the practical use, many variations of SA like Fast SA (FSA) have been developed for faster convergence. In this paper, we propose and prove that Greedy SA (GSA) also finds the global optimum in probability in the continuous space optimization problems. Because the greedy selection does not allow the cost to become worse, GSA is expected to have faster convergence than the conventional FSA that uses Metropolis selection. In the computer simulation, the proposed method is shown to have as good performance as FSA with Metropolis selection in the viewpoints of the convergence speed and the quality of the found solution. Furthermore, the greedy selection does not concern the cost value itself but uses only dominance of the costs of solutions, which makes GSA invariant to the problem scaling.

• The Korean Journal of Applied Statistics
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• v.19 no.1
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• pp.149-161
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• 2006

The most important component in decision tree algorithm is the rule for split variable selection. Many earlier algorithms such as CART and C4.5 use greedy search algorithm for variable selection. Recently, many methods were developed to cope with the weakness of greedy search algorithm. Most algorithms have different selection criteria depending on the type of variables: continuous or nominal. However, ordinal type variables are usually treated as continuous ones. This approach did not cause any trouble for the methods using greedy search algorithm. However, it may cause problems for the newer algorithms because they use statistical methods valid for continuous or nominal types only. In this paper, we propose a ordinal variable selection method that uses Cramer-von Mises testing procedure. We performed comparisons among CART, C4.5, QUEST, CRUISE, and the new method. It was shown that the new method has a good variable selection power for ordinal type variables.

• Journal of the Korea Society of Computer and Information
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• v.29 no.4
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• pp.1-8
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• 2024

The traveling salesman problem(TSP) is one of the well known combinatorial optimization problems. Local search has been used as a method to solve TSP. Greedy Random Insertion(GRI) is known as an effective neighbor generation method for local search. GRI selects some cities from the current solution randomly and inserts them one by one into the best position of the current partial solution considering only one city at a time. We first propose another greedy neighbor generation method which is named Full Greedy Insertion(FGI). FGI determines insertion location one by one like GRI, but considers all remaining cities at once. And then we propose a method to combine GRI with FGI, in which GRI or FGI is randomly selected and executed at each iteration in simulated annealing. According to the experimental results, FGI alone does not necessarily perform very well. However, we confirmed that the combined method outperforms the existing local search methods including GRI.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.69-76
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• 2022

The traveling salesman problem(TSP) is one of the most famous combinatorial optimization problem. So far, many metaheuristic search algorithms have been proposed to solve the problem, and one of them is local search. One of the very important factors in local search is neighbor generation method, and random-based neighbor generation methods such as inversion have been mainly used. This paper proposes 4 new greedy-based neighbor generation methods. Three of them are based on greedy insertion heuristic which insert selected cities one by one into the current best position. The other one is based on greedy rotation. The proposed methods are applied to first-choice hill-climbing search and simulated annealing which are representative local search algorithms. Through the experiment, we confirmed that the proposed greedy-based methods outperform the existing random-based methods. In addition, we confirmed that some greedy-based methods are superior to the existing local search methods.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.11
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• pp.5238-5244
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• 2011

Recently, wireless sensor networks have become increasingly interesting areas over extensive application fields such as military, ecological, and health-related areas. Almost sensor networks have mission-critical tasks that requires very high security. Therefore, extensive work has been done for securing sensor networks from outside attackers, efficient cryptographic systems, secure key management and authorization, but little work has yet been done to protect these networks from inside threats. This paper proposed an method to select which nodes should activate their idle nodes as detectors to be able to watch all packets in the sensor network. Suggested method is modeled as optimization equation, and heuristic Greedy algorithm based simulation results are presented to verify my approach.

• The Journal of the Korea Contents Association
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• v.10 no.5
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• pp.1-9
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• 2010

This paper propose a novel scheme called selective evaluation to improve convergence of ACO (ant colony optimization) for feature selection. The scheme cutdown the computational load by excluding the evaluation of unnecessary or less promising candidate solutions. The scheme is realizable in ACO due to the valuable information, pheromone trail which helps identify those solutions. With the aim of checking applicability of algorithms according to problem size, we analyze the timing requirements of three popular feature selection algorithms, greedy algorithm, genetic algorithm, and ant colony optimization. For a rigorous timing analysis, we adopt the concept of atomic operation. Experimental results showed that the ACO with selective evaluation was promising both in timing requirement and recognition performance.

• Convergence Security Journal
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• v.10 no.1
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• pp.41-48
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• 2010

Greedy protocols show good performance in Vehicular Ad-hoc Networks (VANETs) environment in general. But they make longer routes causing by surroundings or turn out routing failures in some cases when there are many traffic signals which generate empty streets temporary, or there is no merge roads after a road divide into two roads. When a node selects the next node simply using the distance to the destination node, the longer route is made by traditional greedy protocols in some cases and sometimes the route ends up routing failure. Most of traditional greedy protocols just take into account the distance to the destination to select a next node. Each node needs to consider not only the distance to the destination node but also the direction to the destination while routing a packet because of geographical environment. The proposed routing scheme considers both of the distance and the direction for forwarding packets to make a stable route. And the protocol can configure as the surrounding environment. We evaluate the performance of the protocol using two mobility models and network simulations. Most of network performances are improved rather than in compared with traditional greedy protocols.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.46 no.11
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• pp.25-31
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• 2009

In this paper we propose an improved energy aware greedy perimeter stateless routing protocol (EAGPSR) for wireless ad hoc network. The existing greedy perimeter stateless routine (GPSR) has some problems with overloaded node and void situation. The improved EAGPSR protocol is proposed to remedy these problems. It also gives the solution for the fundamental problem in geographical routine called void communication. It considers two parameters (Residual Energy of battery and distance to the destination) for the next hop selection. In order to use efficiently limited-energy of node in wireless ad hoc network, network lifetime is focused. To evaluate the performance of our protocol we simulated EAGPSR in ns-2. The simulation results show that the proposed protocol achieves longer network lifetime compared with greedy perimeter stateless routing (GPSR) and the existing Energy aware greedy perimeter stateless routing protocol (EAGPSR).

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.2
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• pp.367-374
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• 2022

A sampling set selection algorithm is proposed to reconstruct original graph signals from the sampled signals generated on the nodes in the sampling set. Instead of directly minimizing the reconstruction error, we focus on minimizing the upper bound on the reconstruction error to reduce the algorithm complexity. The metric is manipulated by using QR factorization to produce the upper triangular matrix and the analytic result is presented to enable a greedy selection of the next nodes at iterations by using the diagonal entries of the upper triangular matrix, leading to an efficient sampling process with reduced complexity. We run experiments for various graphs to demonstrate a competitive reconstruction performance of the proposed algorithm while offering the execution time about 3.5 times faster than one of the previous selection methods.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2003.05a
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• pp.429-434
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• 2003

본 논문은 작업일정계획에서 부하평준화 문제를 효율적으로 해결하기 위하여 tabu 탐색을 적용함에 있어서 확률적 선별에 기반하여 이웃해를 생성하는 방법을 제시한다. 이웃해 생성은 부하평준화를 위해 일정을 조정할 대상 작업을 선택하는 단계와 선택된 작업에 대해 일정 조정의 방향을 결정하는 단계로 구분된다. 확률적 선별에 기반한 이웃해 생성은 우선 무작위로 추출된 작업에 대해서 탐색의 질을 개선시킬 수 있는 가능성에 대한 추정치에 따라 확률을 부여하고, 이 확률에 기반하여 선택여부를 결정함으로써 이웃해를 선별하는 방법이다. 실제 현장의 부하평준화 문제를 대상으로 이웃해 생성 방법으로 무작위 방법, 그리디(greedy) 방법과의 비교 실험을 통해 확률적 선별에 기반한 이웃해 생성 방법의 성능을 검증하였다.