• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.2
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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.

• The Journal of the Korea Contents Association
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• v.21 no.7
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• pp.164-170
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• 2021

In VLSI design, partitioning is a task of clustering objects into groups so that a given objective circuit is optimized. 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 partitioning include the Kernighan-Lin algorithm, Fiduccia-Mattheyses heuristic and simulated annealing. In this paper, we propose a adapted genetic algorithm searching solution space for the circuit partitioning problem, and then compare it with simulated annealing and genetic algorithm by analyzing the results of implementation. As a result, it was found that an adaptive genetic algorithm approaches the optimal solution more effectively than the simulated annealing and genetic algorithm.

• Computers and Concrete
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• v.11 no.3
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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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• v.7 no.1
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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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• v.9 no.8
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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.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.12
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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.

• Proceedings of the IEEK Conference
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• 2002.07b
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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.

• Journal of Korean Society for Quality Management
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• v.28 no.2
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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.

• Proceedings of the Korea Contents Association Conference
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• 2006.11a
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• pp.811-814
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• 2006

There are Monsters for game player in computer game. The Monsters generated by steady methods and data. And There are few methods that can be adapted to environment or player. This paper introduces a reformed Genetic Algorithm for Monster generation. This algorithm is applied to Homologous Chromosomes(HC). In existing GAs, An Individual have only one genome. But, In proposed algorithm, each Individual has a pair of allele genes on each locus. To compare proposed algorithm with Simple Genetic algorithm, I simulated the solution of a simple Binary problem. After experiments, I conclude that the suggested Algorithm reduced the number of generations more than SGA.

• International Journal of Computer Science & Network Security
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• v.21 no.12
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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.