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An Efficient Genetic Algorithm for Maximum Coverage Deployment in Wireless Sensor Networks
Yourim Yoon,Yong-Hyuk Kim IEEE 2013 IEEE transactions on cybernetics Vol.43 No.5
<P>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.</P>
A Memetic Lagrangian Heuristic for the 0-1 Multidimensional Knapsack Problem
Yoon, Yourim,Kim, Yong-Hyuk Hindawi Limited 2013 Discrete dynamics in nature and society Vol.2013 No.-
<P>We present a new evolutionary algorithm to solve the 0-1 multidimensional knapsack problem. We tackle the problem using duality concept, differently from traditional approaches. Our method is based on Lagrangian relaxation. 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 capacity constraints of the problem. Through empirical investigation of Lagrangian space, we can see the potentiality of using a memetic algorithm. So we use a memetic algorithm to find the optimal Lagrange multipliers. We show the efficiency of the proposed method by the experiments on well-known benchmark data.</P>
Quotient geometric crossovers and redundant encodings
Yoon, Yourim,Kim, Yong-Hyuk,Moraglio, Alberto,Moon, Byung-Ro Elsevier 2012 Theoretical computer science Vol.425 No.-
<P><B>Abstract</B></P><P>We extend a geometric framework for the interpretation of search operators to encompass the genotype–phenotype mapping derived from an equivalence relation defined by an isometry group. We show that this mapping can be naturally interpreted using the concept of quotient space, in which the original space corresponds to the genotype space and the quotient space corresponds to the phenotype space. Using this characterization, it is possible to define induced geometric crossovers on the phenotype space (called <I>quotient geometric crossovers</I>). These crossovers have very appealing properties for non-synonymously redundant encodings, such as reducing the size of the search space actually searched, removing the low locality from the encodings, and allowing a more informed search by utilizing distances better tailored to the specific solution interpretation. Interestingly, quotient geometric crossovers act on genotypes but have an effect equivalent to geometric crossovers acting directly on the phenotype space. This property allows us to actually implement them even when phenotypes cannot be represented directly. We give four example applications of quotient geometric crossovers for non-synonymously redundant encodings and demonstrate their superiority experimentally.</P>
윤유림(Yourim Yoon),김용혁(Yong-Hyuk Kim) 한국지능시스템학회 2010 한국지능시스템학회논문지 Vol.20 No.3
본 논문에서는 실세계에서 센서를 배치할 때 발생하는 최적화 문제인 최대 커버리지 센서 배치 문제를 정의하고 문제의해 공간의 특성을 분석하였다. 또한 최대 커버리지 센서 배치 문제의 좋은 해를 얻기 위해 유전 알고리즘을 설계하고 그 우수성을 비교 실험을 통해 보였다. 이 문제에 유전 알고리즘을 적용할 때 중요하게 고려되어야 할 부분은 평가 함수를 어떻게 구현하느냐 인데 몬테카를로법을 통해 해결할 수 있었다. 유전 알고리즘의 몬테카를로법을 이용한 평가 부분에서 샘플 생성 횟수를 조절함으로써 동일한 성능을 내면서 계산 시간을 크게 줄일 수 있었다. In this paper, we formally define the problem of maximizing the coverage of sensor deployment, which is the optimization problem appeared in real-world sensor deployment, and analyze the properties of its solution space. To solve the problem, we proposed novel genetic algorithms, and we could show their superiority through experiments. When applying genetic algorithms to maximum coverage sensor deployment, the most important issue is how we evaluate the given sensor deployment efficiently. We could resolve the difficulty by using Monte Carlo method. By regulating the number of generated samples in the Monte Carlo evaluation of genetic algorithms, we could also reduce the computing time significantly without loss of solution quality.
윤유림(Yourim Yoon),김용혁(Yong-Hyuk Kim) 한국지능시스템학회 2010 한국지능시스템학회논문지 Vol.20 No.6
일반적으로 이산 최적화에서의 라그랑지안 방법은 제약조건을 쉽게 다루기 위한 기법이다. 이 방법은 전형적으로 분지한계법에서 상한을 찾을 때 사용한다. 본 논문은 여러 개의 제약조건이 있는 다중 배낭 문제를 위한 새로운 라그랑지안 방법을 제안한다. 기존 라그랑지안 접근법과는 달리 제안한 방법은 라그랑지안 벡터의 새로운 특징에 기초하여 품질 좋은 하한 (즉, 가능 해)을 효율적으로 찾을 수 있다. 잘 알려진 큰 규모의 벤치마크 데이터에서 실험을 하였고 제안한 라그랑지안 방법은 기존 방법의 성능을 개선하였다. In general, Lagrangian method for discrete optimization is a kind of technique to easily manage constraints. It is traditionally used for finding upper bounds in the branch-and-bound method. In this paper, we propose a new Lagrangian search method for the 0-1 knapsack problem with multiple constraints. A novel feature of the proposed method different from existing Lagrangian approaches is that it can find high-quality lower bounds, i.e., feasible solutions, efficiently based on a new property of Lagrangian vector. We show the performance improvement of the proposed Lagrangian method over existing ones through experiments on well-known large scale benchmark data.
Sunkyung Yoon,Yourim Kim,Seung-Hwan Lee 대한정신약물학회 2021 CLINICAL PSYCHOPHARMACOLOGY AND NEUROSCIENCE Vol.19 No.2
Objective: Loudness of dependence of the auditory evoked potential (LDAEP) is an electroencephalogram-based meas-ure that represents amplitude changes of auditory evoked potentials in primary auditory cortex. Several narrative reviews argued that pre-treatment LDAEP values predict responses to selective serotonin reuptake inhibitors (SSRIs). This study aims to quantify the overall relationship between baseline LDAEP values and treatment response to SSRIs in patients with depression and generalized anxiety disorders, evidenced by clinical symptoms reductions, across multiple studies. Methods: In our meta-analysis, seven articles with a total sample of 241 patients were included. Results: Our results showed that stronger baseline LDAEP values predicted favorable response to SSRIs for depression and anxiety, with a moderate effect size. Conclusion: The current results support the idea that LDAEP is a promising biomarker for SSRIs treatment prediction in patients with depression and generalized anxiety disorder.
Effect of Changing the Basis in Genetic Algorithms Using Binary Encoding
( Yong-hyuk Kim ),( Yourim Yoon ) 한국인터넷정보학회 2008 KSII Transactions on Internet and Information Syst Vol.2 No.4
We examine the performance of genetic algorithms using binary encoding, with respect to a change of basis. Changing the basis can result in a change in the linkage structure inherent in the fitness function. We test three simple functions with differing linkage strengths and analyze the results. Based on an empirical analysis, we show that a better basis results in a smoother fitness landscape, hence genetic algorithms based on the new encoding method provide better performance.