• Journal of applied mathematics & informatics
• /
• v.26 no.3_4
• /
• pp.493-500
• /
• 2008

In this paper, we first construct a mathematical model for tagSNP selection based on LD measure $r^2$, then aiming at this kind of model, we develop an efficient algorithm, which is called approximate greedy algorithm. This algorithm is able to make up the disadvantage of the greedy algorithm for tagSNP selection. The key improvement of our approximate algorithm over greedy algorithm lies in that it adds local replacement(or local search) into the greedy search, tagSNP is replaced with the other SNP having greater similarity degree with it, and the local replacement is performed several times for a tagSNP so that it can improve the tagSNP set of the local precinct, thereby improve tagSNP set of whole precinct. The computational results prove that our approximate greedy algorithm can always find more efficient solutions than greedy algorithm, and improve the tagSNP set of whole precinct indeed.

• Journal of the Institute of Convergence Signal Processing
• /
• v.3 no.3
• /
• pp.63-69
• /
• 2002

Detailed routing is a problem assigning each net to a track after global routing. The most popular algorithms for detailed routing include left-edge algorithm, dogleg algorithm, and greedy channel routing algorithm. In this paper we propose a genetic algorithm searching solution space for the detailed routing problem. We compare the performance of proposed genetic algorithm(GA) for detailed routing with that of greedy channel routing algorithm by analyzing the results of each implementation.

• Journal of the Korea Society for Simulation
• /
• v.28 no.1
• /
• pp.67-79
• /
• 2019

A warship equipped with Multi Function Radar(MFR) performs operations by evaluating the degree of threats based on threats' symptom and allocating the resource of MFR to the corresponding threats. This study suggests a simulation-based approach and greedy algorithm in order to effectively allocate the resource of an MFR for warships, and compares these two approaches. As a detection probability function depending on the amount of allocations to each threat, we consider linear and exponential functions. Experimental results show that both the simulation-based approach and greedy algorithm allocate resource similarly to the randomly generated threats, and the greedy algorithm outperforms the simulation-based approach in terms of computational perspective. For a various cases of threats, we analyze the results of MFR resource allocation using the greedy algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.33 no.12B
• /
• pp.1075-1082
• /
• 2008

A greedy forwarding algorithm is one of the most suitable solutions for routing in vehicular ad-hoc networks. Compared to conventional routing protocols for mobile ad-hoc networks, greedy forwarding based routing protocols maintain only local information of neighbors instead of per-destination routing entries, and thus they show better performance in highly-mobile vehicular ad-hoc networks. With greedy forwarding, each node learns its geographical position and periodically broadcasts a beacon message including its position information. Based on the position information, each node selects a neighbor node located closest to the destination node as the next forwarder. One of the most serious problems in greedy forwarding is the lost link problem due to the mobility of nodes. In this paper, we propose a new algorithm to reduce the lost link problem. The proposed algorithm aims to find an efficient and stable routing path by taking account of the position of neighbors and the last beacon reception time. Our simulation results show that the proposed algorithm outperforms the legacy greedy algorithm and its variants.

• Journal of Korean Institute of Industrial Engineers
• /
• v.11 no.2
• /
• pp.155-163
• /
• 1985

This paper presents a heuristic algorithm for a multidepot aircraft scheduling and crew scheduling with deal-head flights. This algorithm is extended from a Greedy heuristic algorithm for a multi-depot multi-salesman traveling salesman problem. We first transform a given flight schedule into a multi-depot multi-traveling salesman problem, considering aircraft flight policies and crew management constraints. Then we solve this problem by applying a modified Greedy heuristic algorithm.

• The Transactions of the Korea Information Processing Society
• /
• v.7 no.3
• /
• pp.935-942
• /
• 2000

In this paper we introduce a fast simplification algorithm for terrain height fields to produce a triangulated irregular network, based on the greedy insertion algorithm in [1,4,5]. Our algorithm partitions a terrain height data into rectangular blocks with the same size ad simplifies blocks one by one with the greedy insertion algorithm. Our algorithm references only to the points and the triangles withing each current block for adding a point into the triangulation. Therefore, the algorithm runs faster than the greedy insertion algorithm, which references all input points and triangles in the terrain. Our experiment shows that partitioning method runs from 4 to more than 20 times faster, and it approximates test height fields as accurately as the greedy insertion algorithms. Most greedy insertion algorithms suffer from elongated triangles that usually appear near the boundaries. However, we insert the four corner points into each block to produce the base triangulation of the block before the point addition step begins so that elongated triangles could not appear in th simplified terrain.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.36 no.3
• /
• pp.17-24
• /
• 2013

Release planning in a software product line (SPL) is to select and assign the features of the multiple software products in the SPL in sequence of releases along a specified planning horizon satisfying the numerous constraints regarding technical precedence, conflicting priorities for features, and available resources. A greedy genetic algorithm is designed to solve the problems of release planning in SPL which is formulated as a precedence-constrained multiple 0-1 knapsack problem. To be guaranteed to obtain feasible solutions after the crossover and mutation operation, a greedy-like heuristic is developed as a repair operator and reflected into the genetic algorithm. The performance of the proposed solution methodology in this research is tested using a fractional factorial experimental design as well as compared with the performance of a genetic algorithm developed for the software release planning. The comparison shows that the solution approach proposed in this research yields better result than the genetic algorithm.

• Journal of Information Processing Systems
• /
• v.10 no.3
• /
• pp.471-482
• /
• 2014

Influence maximization is an important problem of finding a small subset of nodes in a social network, such that by targeting this set, one will maximize the expected spread of influence in the network. To improve the efficiency of algorithm KK_Greedy proposed by Kempe et al., we propose two improved algorithms, Lv_NewGreedy and Lv_CELF. By combining all of advantages of these two algorithms, we propose a mixed algorithm Lv_MixedGreedy. We conducted experiments on two synthetically datasets and show that our improved algorithms have a matching influence with their benchmark algorithms, while being faster than them.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.23 no.4
• /
• pp.381-388
• /
• 2020

Weapon target assignment problem is an essential technology for automating the operator's rapid decision-making support in a battlefield situation. Weapon target assignment problem is a kind of the optimization problem that can build up an objective function by maximizing the number of threat target destructed or maximizing the survival rate of the protected assets. Weapon target assignment problem is known as the NP-Complete, and various studies have been conducted on it. Among them, a greedy heuristic algorithm which guarantees (1-1/e) approximation has been considered a very practical method in order to enhance the applicability of the real weapon system. In this paper, we formulated the weapon target assignment problem for supporting decision-making at the level of artillery. The lazy strategy based on hierarchical structure is proposed to accelerate the greedy algorithm. By experimental results, we show that our algorithm is more efficient in processing time and support the same level of the objective function value with the basic greedy algorithm.

• Bulletin of the Korean Mathematical Society
• /
• v.38 no.2
• /
• pp.293-302
• /
• 2001

We improve the greedy algorithm which is one of the general convergence criterion for certain iterative sequence in a given space by building a constructive greedy algorithm on a normed linear space using an arithmetic average of elements. We also show the degree of approximation order is still $Ο(1\sqrt{\n}$) by a bounded linear functional defined on a bounded subset of a normed linear space which offers a good approximation method for neural networks.