• 디지털산업정보학회논문지
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• 제17권2호
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• pp.13-20
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• 2021

Placement is a very important step in the VLSI physical design process. It is the problem of placing circuit modules to optimize the circuit performance and reliability of the circuit. It is used at the layout level to find strongly connected components that can be placed together in order to minimize the layout area and propagation delay. The most popular algorithms for circuit placement include the cluster growth, simulated annealing, integer linear programming and genetic algorithm. In this paper we propose a adapted genetic algorithm searching solution space for the placement problem, and then compare it with simulated annealing and genetic algorithm by analyzing the results of each implementation. As a result, it was found that the adaptive genetic algorithm approaches the optimal solution more effectively than the simulated annealing and genetic algorithm.

• 한국콘텐츠학회논문지
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• 제21권7호
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• pp.164-170
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• 2021

VLSI 설계에서의 분할(partitioning)은 기능의 최적화를 위하여 설계하고자 하는 회로의 그룹화(grouping)하는 단계로서 레이아웃(layout)에서 면적과 전파지연의 최소화를 위해 함께 배치할 소자를 결정하는 문제이다. 이러한 분할 문제에서 해를 얻기 위해 사용되는 알고리즘은 Kernighan-Lin 알고리즘, Fiduccia Mattheyses heuristic, 시뮬레이티드 어닐링, 유전자 알고리즘 등의 방식이 이용된다. 본 논문에서는 회로 분할 문제에 대하여 유전자 알고리즘과 확률 진화 알고리즘을 결합한 어댑티드 유전자 알고리즘을 이용한 해 공간 탐색(solution space search) 방식을 제안하였으며, 제안한 방식을 유전자 알고리즘 및 시뮬레이티드 어닐링 방식과 비교, 분석하였고, 어댑티드 유전자 알고리즘이 시뮬레이티드 어닐링 및 유전자 알고리즘보다 더 효과적으로 최적해에 근접하는 것을 알 수 있었다.

• Computers and Concrete
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• 제11권3호
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• pp.183-199
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• 2013

To select a most desired mix proportion that meets required performances according to the quality of recycled aggregate, a large number of experimental works must be carried out. This paper proposed a new design method for the mix proportion of recycled aggregate concrete to reduce the number of trial mixes. Genetic algorithm is adapted for the method, which has been an optimization technique to solve the multi-criteria problem through the simulated biological evolutionary process. Fitness functions for the required properties of concrete such as slump, density, strength, elastic modulus, carbonation resistance, price and carbon dioxide emission were developed based on statistical analysis on conventional data or adapted from various early studies. Then these fitness functions were applied in the genetic algorithm. As a result, several optimum mix proportions for recycled aggregate concrete that meets required performances were obtained.

• Communications for Statistical Applications and Methods
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• 제7권1호
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• pp.357-370
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• 2000

This study has adapted a genetic algorithm for an optimal design for the first time. the models that was used a simulation are the first and second order response surfaces model, Using an genetic algorithm in D-opimal it is more efficient than previous algorithms to get an object function. Not like other algorithm without any restrictions like troublesome about the initial solution not falling into a local optimal solution it's the most suitable algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권8호
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• pp.2821-2839
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• 2015

Energy consumption by large computing systems has become an important research theme not only because the sources of energy are depleting fast but also due to the environmental concern. Computational grid is a huge distributed computing platform for the applications that require high end computing resources and consume enormous energy to facilitate execution of jobs. The organizations which are offering services for high end computation, are more cautious about energy consumption and taking utmost steps for saving energy. Therefore, this paper proposes a scheduling technique for Minimizing Energy consumption using Adapted Genetic Algorithm (MiE-AGA) for dependent tasks in Computational Grid (CG). In MiE-AGA, fitness function formulation for energy consumption has been mathematically formulated. An adapted genetic algorithm has been developed for minimizing energy consumption with appropriate modifications in each components of original genetic algorithm such as representation of chromosome, crossover, mutation and inversion operations. Pseudo code for MiE-AGA and its components has been developed with appropriate examples. MiE-AGA is simulated using Java based programs integrated with GridSim. Analysis of simulation results in terms of energy consumption, makespan and average utilization of resources clearly reveals that MiE-AGA effectively optimizes energy, makespan and average utilization of resources in CG. Comparative analysis of the optimization performance between MiE-AGA and the state-of-the-arts algorithms: EAMM, HEFT, Min-Min and Max-Min shows the effectiveness of the model.

• 대한전기학회논문지:시스템및제어부문D
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• 제49권12호
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• pp.667-674
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• 2000

In this paper, a modified real-valued genetic algorithm is developed by using the meiosis for human's chromosome. Unlike common crossover methods adapted in the conventional genetic algorithms, our suggested modified real-valued genetic algorithm makes gametes by conducting the meiosis for individuals composed of chromosomes, and then generates a new individual through crossovers among those. Ultimately, when appling it for the gas data of Box-Jenkin, model and parameter identifications can be concurrently done to construct the optimal model of a neural network in terms of minimizing with the structure and the error.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -2
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• pp.1176-1179
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• 2002

This paper presents the use of a Genetic Algorithm to find the optimal layout for the placement of garment patterns on a fabric of fixed width to minimize fabric waste. We developed a program to simulate garment pieces and their layout on a fixed-width fabric. Each piece in the order book is placed with 2 possible orientations: 0 degrees and 180 degrees. The efficiency is measured by the length of fabric used after all the patterns in the order book have been laid out. A comparison is made between the placement using our proposed genetic algorithm to that made by an expert human using our simulation program. The results from our experiments on various pattern designs indicate that our genetic algorithm can effectively be used to obtain highly efficient solutions, comparable to that done by an expert while using a reasonable amount of time. The algorithm can also be adapted for use in other areas related to optimal consumption of sheet material such as metal, paper, and leather.

• 품질경영학회지
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• 제28권2호
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• pp.135-146
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• 2000

This study has adapted a genetic algorithm for an optimal design for the first time. The models using a simulation are the nonlinear models. Using an genetic algorithm in D-optimal, it is more efficient than previous algorithms to get an object function. Not like other algorithms, without any troublesome restrictions about the initial solution, not falling into a local optimal solution, it's the most suitable algorithm. Also if we use it without any adding experiments, we can use it to find optimal design of experimental condition efficiently.

• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2006년도 추계 종합학술대회 논문집
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• pp.811-814
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• 2006

대부분의 게임에는 플레이어를 상대하는 몬스터가 존재한다. 이 몬스터는 대부분 미리 정해진 방법과 데이터로 생성되며, 환경이나 플레이어에 적응하는 방식은 거의 없었다. 본 논문에서는 몬스터 생성을 위해 개선된 유전 알고리즘을 소개한다. 이 알고리즘은 상동염색체 구조가 적용되어있다. 기존의 유전알고리즘에서 각 개체가 오직 하나의 genome만을 가지고 있다. 하지만, 상동 염색체 구조를 가지고 있는 유전 알고리즘에서는 각 개체가 각 좌 위에 한 쌍의 대립 유전자를 지니게 된다. 단순한 유전알고리즘과 개선된 유전알고리즘을 비교하기 위해 간단한 이진 문제를 가지고 시뮬레이션 해 보았다. 실험결과 제안된 알고리즘이 더 적은 세대수로 답을 찾을 수 있다는 것을 알게 되었다.

• International Journal of Computer Science & Network Security
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• 제21권12호
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• pp.175-182
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• 2021

Deadlock is a situation in which two or more processes competing for resources are waiting for the others to finish, and neither ever does. There are two different forms of systems, multi-unit and single-unit resource systems. The difference is the number of instances (or units) of each type of resource. Deadlock problem can be modeled as a constrained combinatorial problem that seeks to find a possible scheduling for the processes through which the system can avoid entering a deadlock state. To solve deadlock problem, several algorithms and techniques have been introduced, but the use of metaheuristics is one of the powerful methods to solve it. Genetic algorithms have been effective in solving many optimization issues, including deadlock Problem. In this paper, an improved parallel framework of the genetic algorithm is introduced and adapted effectively and efficiently to deadlock problem. The proposed modified method is implemented in java and tested on a specific dataset. The experiment shows that proposed approach can produce optimal solutions in terms of burst time and the number of feasible solutions in each advanced generation. Further, the proposed approach enables all types of crossovers to work with high performance.