• The Journal of the Convergence on Culture Technology
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• v.6 no.4
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• pp.721-726
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• 2020

Genetic algorithm was first introduced to study adaption phenomena occurring in nature based on Darwin's theory of survival of the fittest. It has been used when analytical approach is not possible because of a large number of variables. In this paper, we demonstrated that genetic algorithm could be used to obtain physically optimized experimental values. We programmed a genetic algorithm that uses a few Gaussian functions to find a given function value and the same algorithm was connected to the laser system to obtain laser pulses of 40fs of maximum pulse width and 1mJ of maximum output power. This study shows that genetic algorithm can be applied to laser systems to obtain the optimized laser pulses.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.79-87
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• 2023

In this paper, we propose a novel genetic algorithm to reduce the overall span time by optimizing the skid insertion sequence in the shipyard subassembly process. We represented a solution by a permutation of a set of skid ids and applied genetic operators suitable for such a representation. In addition, we combined the genetic algorithm and the existing heuristic algorithm called UniDev which is properly modified to improve the search performance. In particular, the slow skid search part in UniDev was changed to a greedy algorithm. Through extensive large-scaled simulations, it was observed that the span time of our method was stably minimized compared to Multi-Start search and a genetic algorithm combined with UniDev.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.6
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• pp.1008-1015
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• 2002

This paper proposes a combinatorial method to compute the global and local solutions of optimization problem. The present hybrid algorithm is the synthesis of a genetic algorithm and a local concentrate search algorithm (simplex method). The hybrid algorithm is not only faster than the standard genetic algorithm, but also gives a more accurate solution. In addition, this algorithm can find both the global and local optimum solutions. An optimization result is presented to demonstrate that the proposed approach successfully focuses on the advantages of global and local searches. Three numerical examples are also presented in this paper to compare with conventional methods.

• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.10 no.4
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• pp.128-134
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• 2011

Note that k-anonymity algorithm has been widely discussed in the area of privacy protection. In this paper, a new search algorithm to achieve k-anonymity for database application is introduced. A lattice is introduced to form a solution space for a k-anonymity problem and then a hybrid search method composed of tabu search and genetic algorithm is proposed. In this algorithm, the tabu search plays the role of mutation in the genetic algorithm. The hybrid method with independent tabu search and genetic algorithm is compared, and the hybrid approach performs the best in average case.

• Proceedings of the KIEE Conference
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• 2002.11c
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• pp.169-172
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• 2002

This paper is concerned with walking trajectory generation by applying the genetic algorithm. The walking trajectory is generated though three via-points and genetic algorithm is employed to find velocity and acceleration at each via-point. Also genetic algorithm is applied for balancing joint trajectory. Fitness function is used for minimizing the trajectory. As a result, new algorithm generated the smooth trajectory. The proposed algorithm is verified by the experiment of biped walking robot developed in our Control laboratory, and we compared the result with the previous walking algorithm. It showed that the new proposed algorithm generated the better walking trajectory.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.3
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• pp.71-79
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• 1998

This paper introduces a new optimization algorithm which is combined with genetic algorithm and random tabu search method. Genetic algorithm is a random search algorithm which can find the global optimum without converging local optimum. And tabu search method is a very fast search method in convergent speed. The optimizing ability and convergent characteristics of a new combined optimization algorithm is identified by using a test function which have many local optimums and an optimum allocation of pipe support. The caculation results are compared with the existing genetic algorithm.

• Journal of Information Processing Systems
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• v.16 no.5
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• pp.1169-1182
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• 2020

The ant colony optimization (ACO) algorithm is a classical metaheuristic optimization algorithm. However, the conventional ACO was liable to trap in the local minimum and has an inherent slow rate of convergence. In this work, we propose a novel combinatorial ACO algorithm (CG-ACO) to alleviate these limitations. The genetic algorithm and the cloud model were embedded into the ACO to find better initial solutions and the optimal parameters. In the experiment section, we compared CG-ACO with the state-of-the-art methods and discussed the parameter stability of CG-ACO. The experiment results showed that the CG-ACO achieved better performance than ACO_R, simple genetic algorithm (SGA), CQPSO and CAFSA and was more likely to reach the global optimal solution.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.2
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• pp.287-295
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• 2010

In this paper, we introduce a novel approach to optimization algorithm which is a distributed Mean field Genetic algorithm (MGA) implemented in MPI(Message Passing Interface) environments. Distributed MGA is a hybrid algorithm of Mean Field Annealing(MFA) and Simulated annealing-like Genetic Algorithm(SGA). The proposed distributed MGA combines the benefit of rapid convergence property of MFA and the effective genetic operations of SGA. The proposed distributed MGA is applied to the channel routing problem, which is an important issue in the automatic layout design of VLSI circuits. Our experimental results show that the composition of heuristic methods improves the performance over GA alone in terms of mean execution time. It is also proved that the proposed distributed algorithm maintains the convergence properties of sequential algorithm while it achieves almost linear speedup as the problem size increases.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.5
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• pp.977-984
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• 2001

In this paper, Genetic-Fuzzy Algorithm for Inverted Pendulum is presented. This Algorithms is combine Fuzzy logic with the Genetic Algorithm. The Fuzzy Logic Controller is only designed to two inputs and one output. After Fuzzy control rules are determined, Genetic Algorithm is applied to tune the membership functions of these rules. To measure of performance of the designed Genetic-Fuzzy controller, Computer simulation is applied to Inverted Pendulum system. In the simulation, In the case of f[0.3, 0.3] Fuzzy controller is measured that maximum undershoot is $-5.0 \times 10^{-2}[rad]$, maximum undershoot is $3.92\times10^{-2}[rad]$ individually however, Designed algorithm is zero. The Steady state time is approximated that Fuzzy controller is 2.12[sec] and designed algorithm is 1.32[sec]. The result of simulation, Resigned algorithm is showed it's efficient and effectiveness for Inverted Pendulum system.

• Journal of the Korean Society of Radiology
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• v.14 no.1
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• pp.69-75
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• 2020

Multilayer mirrors have widely been used not only in the industry but also in the medical field. X-ray reflectivity was measured by X-ray diffractometer to evaluate the performance of W/C multilayer mirror with 40 layers. Genetic algorithm are used to obtain thickness, density, and interfacial roughness for each of the 40 layers. The existing uniform random selection causes a problem that the solution does not converge or the error increases even if it convergence. To reduce the time to calculate the fitness of the genetic algorithm, the genetic algorithm was written in C/C++ parallel programming. The genetic algorithm showed excellent scalability of linear time increase with increasing number of generation and population. The genetic algorithm was selected with uniform and Gaussian randomness of 1:1 to improve the convergence of solution. The improved genetic algorithm can be applied to characterize each layer of a sample with more than a few tens of layers, such as a multilayer mirror.