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Dive into the research topics where Yourim Yoon is active.

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Featured researches published by Yourim Yoon.


IEEE Transactions on Systems, Man, and Cybernetics | 2013

An Efficient Genetic Algorithm for Maximum Coverage Deployment in Wireless Sensor Networks

Yourim Yoon; Yong-Hyuk Kim

Sensor networks have a lot of applications such as battlefield surveillance, environmental monitoring, and industrial diagnostics. Coverage is one of the most important performance metrics for sensor networks since it reflects how well a sensor field is monitored. In this paper, we introduce the maximum coverage deployment problem in wireless sensor networks and analyze the properties of the problem and its solution space. Random deployment is the simplest way to deploy sensor nodes but may cause unbalanced deployment and therefore, we need a more intelligent way for sensor deployment. We found that the phenotype space of the problem is a quotient space of the genotype space in a mathematical view. Based on this property, we propose an efficient genetic algorithm using a novel normalization method. A Monte Carlo method is adopted to design an efficient evaluation function, and its computation time is decreased without loss of solution quality using a method that starts from a small number of random samples and gradually increases the number for subsequent generations. The proposed genetic algorithms could be further improved by combining with a well-designed local search. The performance of the proposed genetic algorithm is shown by a comparative experimental study. When compared with random deployment and existing methods, our genetic algorithm was not only about twice faster, but also showed significant performance improvement in quality.


electronic commerce | 2007

Geometric crossovers for multiway graph partitioning

Alberto Moraglio; Yong-Hyuk Kim; Yourim Yoon; Byung Ro Moon

Geometric crossover is a representation-independent generalization of the traditional crossover defined using the distance of the solution space. By choosing a distance firmly rooted in the syntax of the solution representation as a basis for geometric crossover, one can design new crossovers for any representation. Using a distance tailored to the problem at hand, the formal definition of geometric crossover allows us to design new problem-specific crossovers that embed problem-knowledge in the search. The standard encoding for multiway graph partitioning is highly redundant: each solution has a number of representations, one for each way of labeling the represented partition. Traditional crossover does not perform well on redundant encodings. We propose a new geometric crossover for graph partitioning based on a labeling-independent distance that filters out the redundancy of the encoding. A correlation analysis of the fitness landscape based on this distance shows that it is well suited to graph partitioning. A second difficulty with designing a crossover for multiway graph partitioning is that of feasibility: in general recombining feasible partitions does not lead to feasible offspring partitions. We design a new geometric crossover for permutations with repetitions that naturally suits partition problems and we test it on the graph partitioning problem. We then combine it with the labeling-independent crossover and obtain a much superior geometric crossover inheriting both advantages.


Chemistry-an Asian Journal | 2011

Designer Nanorings with Functional Cavities from Self-Assembling β-Sheet Peptides

Il-Soo Park; Yourim Yoon; Minseon Jung; Kimoon Kim; SeongByeong Park; Seokmin Shin; Yong-beom Lim; Myongsoo Lee

β-Barrel proteins that take the shape of a ring are common in many types of water-soluble enzymes and water-insoluble transmembrane pore-forming proteins. Since β-barrel proteins perform diverse functions in the cell, it would be a great step towards developing artificial proteins if we can control the polarity of artificial β-barrel proteins at will. Here, we describe a rational approach to construct β-barrel protein mimics from the self-assembly of peptide-based building blocks. With this approach, the direction of the self-assembly process toward the formation of water-soluble β-barrel nanorings or water-insoluble transmembrane β-barrel pores could be controlled by the simple but versatile molecular manipulation of supramolecular building blocks. This study not only delineates the basic driving force that underlies the folding of β-barrel proteins, but also lays the foundation for the facile fabrication of β-barrel protein mimics, which can be developed as nanoreactors, ion- and small-molecule-selective pores, and novel antibiotics.


genetic and evolutionary computation conference | 2005

An evolutionary lagrangian method for the 0/1 multiple knapsack problem

Yourim Yoon; Yong-Hyuk Kim; Byung Ro Moon

We propose a new evolutionary approach to solve the 0/1 multiple knapsack problem. We approach the problem from a new viewpoint different from traditional methods. The most remarkable feature is the Lagrangian method. Lagrange multipliers transform the problem, keeping the optimality as well as decreasing the complexity. However, it is not easy to find Lagrange multipliers nearest to the constraints of the problem. We propose an evolution strategy to find the optimal Lagrange multipliers. Also, we improve the evolution strategy by adjusting its objective function properly. We show the efficiency of the proposed methods by the experiments. We make comparisons with existing general approach on well-known benchmark data.


IEEE Transactions on Knowledge and Data Engineering | 2008

A Lagrangian Approach for Multiple Personalized Campaigns

Yong-Hyuk Kim; Yourim Yoon; Byung Ro Moon

The multicampaign assignment problem is a campaign model to overcome the multiple-recommendation problem that occurs when conducting several personalized campaigns simultaneously. In this paper, we propose a Lagrangian method for the problem. The original problem space is transformed to another simpler one by introducing Lagrange multipliers, which relax the constraints of the multicampaign assignment problem. When the Lagrangian vector is supplied, we can compute the optimal solution under this new environment in O(NK2) time, where N and K are the numbers of customers and campaigns, respectively. This is a linear-time method when the number of campaigns is constant. However, it is not easy to find a Lagrangian vector in exact accord with the given problem constraints. We thus combine the Lagrangian method with a genetic algorithm to find good near-feasible solutions. We verify the effectiveness of our evolutionary Lagrangian approach in both theoretical and experimental viewpoints. The suggested Lagrangian approach is practically attractive for large-scale real-world problems.


genetic and evolutionary computation conference | 2009

Visualizing the search process of particle swarm optimization

Yong-Hyuk Kim; Kang Hoon Lee; Yourim Yoon

It is a hard problem to understand the search process of particle swarm optimization over high-dimensional domain. The visualization depicts the total search process and then it will allow better understanding of how to tune the algorithm. For the investigation, we adopt Sammons mapping, which is a well-known distance-preserving mapping. We demonstrate the usefulness of the proposed methodology by applying it to some function optimization problems.


Discrete Dynamics in Nature and Society | 2013

Two Applications of Clustering Techniques to Twitter: Community Detection and Issue Extraction

Yong-Hyuk Kim; Sehoon Seo; Yong-Ho Ha; Seongwon Lim; Yourim Yoon

Twitter’s recent growth in the number of users has redefined its status from a simple social media service to a mass media. We deal with clustering techniques applied to Twitter network and Twitter trend analysis. When we divide and cluster Twitter network, we can find a group of users with similar inclination, called a “Community.” In this regard, we introduce the Louvain algorithm and advance a partitioned Louvain algorithm as its improved variant. In the result of the experiment based on actual Twitter data, the partitioned Louvain algorithm supplemented the performance decline and shortened the execution time. Also, we use clustering techniques for trend analysis. We use nonnegative matrix factorization (NMF), which is a convenient method to intuitively interpret and extract issues on various time scales. By cross-verifying the results using NFM, we found that it has clear correlation with the actual main issue.


Archive | 2012

The Roles of Crossover and Mutation in Real-Coded Genetic Algorithms

Yourim Yoon; Yong-Hyuk Kim

Recently many studies on evolutionary algorithms using real encoding have been done. They include ant colony optimization (Socha & Dorigo, 2008), artificial bee colony algorithm (Akay & Karaboga, 2010; Kang et al., 2011), evolution strategies (ES) (Beyer, 2001), differential evolution (Das & Suganthan, 2011; Dasgupta et al., 2009; Kukkonen & Lampinen, 2004; 2005; Mezura-Montes et al., 2010; Noman & Iba, 2005; Ronkkonen et al., 2005; Storn & Price, 1997; Zhang et al., 2008), particle swarm optimization (Chen et al., 2007; Huang et al., 2010; Juang et al., 2011; Krohling & Coelho, 2006; l. Sun et al., 2011), and so on. In particular, in the field of ES, we can find many studies based on self-adaptive techniques (Beyer & Deb, 2001; Hansen & Ostermeier, 2001; Igel et al., 2007; 2006; Jagerskupper, 2007; Kita, 2001; Kramer, 2008a;b; Kramer et al., 2007; Meyer-Nieberg & Beyer, 2007; Wei et al., 2011).


genetic and evolutionary computation conference | 2011

Geometric surrogate-based optimisation for permutation-based problems

Alberto Moraglio; Yong-Hyuk Kim; Yourim Yoon

In continuous optimisation, surrogate models (SMs) are used when tackling real-world problems whose candidate solutions are expensive to evaluate. In previous work, we showed that a type of SMs - radial basis function networks (RBFNs) - can be rigorously generalised to encompass combinatorial spaces based in principle on any arbitrarily complex underlying solution representation by extending their natural geometric interpretation from continuous to general metric spaces. This direct approach to representations does not require a vector encoding of solutions, and allows us to use SMs with the most natural representation for the problem at hand. In this work, we apply this framework to combinatorial problems using the permutation representation and report experimental results on the quadratic assignment problem.


Applied Mathematics and Computation | 2013

Geometricity of genetic operators for real-coded representation

Yourim Yoon; Yong-Hyuk Kim

We investigate geometricity of operators for real-coded genetic algorithms. Geometric operator is a representation-independent generalization of the class of traditional mask-based operators for binary strings. It is defined as a function of the distance of the search space seen as a metric space. Although the real-coded representation allows for a very familiar notion of distance, namely the Euclidean distance, there are also other distances suiting it, such as Minkowski distances. Additionally, topological transformations of the real space give rise to further notions of distance. In this paper, we study geometric crossover operators associated with Minkowski spaces in a formal and general setting and show that some pre-existing genetic operators for the real-coded representation are geometric. Although geometric crossover operators defined in the plane are intuitive operators, in higher-dimensional spaces they are not trivial to understand and implement. We derive formally algorithms to implement these geometric crossover operators in the generic n-dimensional case. We compare the performance of the presented operators with existing ones on test functions which are commonly used in the literature.

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Byung Ro Moon

Seoul National University

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Chanju Jung

Seoul National University

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Il-Soo Park

Seoul National University

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Jaeun Choi

Seoul National University

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