• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.1011-1014
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• 1999

In this paper, a new algorithm has been presented that helps to preserve diversity as well as to enhance the convergence speed of the evolutionary programming. This algorithm is based on the cell partitioning of search region for preserving the diversity. Until now, the greater part of researches is not concerned about preserving the diversity of individuals in a population but improving convergence speed. Although these evolutions are started from multi-point search at the early phase, but at the end those search points are swarming about a one-point, the strong candidate. These evolutions vary from the original idea in some points such as multi-point search. In most case we want to find the only one point of the best solution not several points in the vicinity of that. That is why the cell partitioning of search region has been used. By restricting the search area of each individual, the diversity of individual in solution space is preserved and the convergence speed is enhanced. The efficiency of the proposed algorithm has been verified through benchmark test functions.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.5
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• pp.234-240
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• 2001

Audible noise (AN) produced by corona discharges from high voltage transmission lines is one of the more important considerations in line design. Therefore, line designers must pre-determine the AN using prediction formulas. This paper presents the results of applying evolutionary computation techniques using AN data from lines throughout the world to develop new, highly accurate formulas for predicting a A-weighted AN during heavy rain and stable rain from overhead ac lines. Calculated ANs using these new formulas and existing formulas are compared with measured data.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.3
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• pp.389-394
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• 2014

Two evolutionary gait generation methods for Cartesian and Joint coordinates space are compared to develop a fast locomotion for quadruped robots. GA(Genetic Algorithm) based approaches seek to optimize a pre-selected set of parameters for the locus of paw and initial position in cartesian coordinates space. GP(Genetic Programming) based technique generate few joint trajectories using symbolic regression in joint coordinates space as a form of polynomials. Optimization for two proposed methods are executed using Webots simulation for the quadruped robot which is built by Bioloid. Furthermore, simulation results for two proposed methods are analysed in terms of different coordinate spaces.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.36C no.3
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• pp.58-64
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• 1999

Evolutionary programming is mainly characterized by two factors; one is the selection strategy and the other the mutation rule. In this paper, a new mutation rule that is the same form of well-known backpropagation learning rule of neural networks has been presented. The proposed mutation rule adapts the best individual's value as the target value at the generation. The temporal error improves the exploration through guiding the direction of evolution and the momentum speeds up convergence. The efficiency and robustness of the proposed algorithm have been verified through benchmark test functions.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.11a
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• pp.259-262
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• 1997

This paper presents a visual servoing of RV-M2 robot manipulators to track and grasp moving object, using pruned dynamic recurrent neural networks(DRNN). The object is stationary in the robot work space and the robot is tracking and grasping the object by using CCD camera mounted on the end-effector. In order to optimize the structure of DRNN, we decide the node whether delete or add, by mutation probability, first in case of delete node, the node which have minimum sum of input weight is actually deleted, and then in case of add node, the weight is connected according to the number of case which added node can reach the other nodes. Using evolutionary programming(EP) that search the struture and weight of the DRNN, and evolution strategies(ES) which train the weight of neuron, we pruned the net structure of DRNN. We applied the DRNN to the Visual Servoing of a robot manipulators to control position and orientation of end-effector, and the validity and effectiveness of the pro osed control scheme will be verified by computer simulations.

• Journal of the Korea Institute of Military Science and Technology
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• v.10 no.3
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• pp.148-153
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• 2007

In this paper, the evolutionary adaptive wavelet transform(EAWT) is applied to the scattering analysis of radar target. EAWT algorithm uses evolutionary programming for the time-frequency parameter extraction instead of FFT and the bisection search method used in the conventional adaptive wavelet transform(AWT). Therefore, the EAWT has a better performance than the conventional AWT. In the simulation using wire target(Airbus-like), the comparisons with the conventional AWT are presented to show the superiority of the EAWT algorithm in the analysis of scattering phenomenology. The EAWT can be effectively applied to the radar target recognition.

• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1441-1452
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• 2015

This paper proposes a new Hybrid Particle Swarm Optimization (HPSO) method that integrates the Evolutionary Programming (EP) and Particle Swarm Optimization (PSO) techniques. The proposed method is applied to solve Economic Dispatch(ED) problems considering prohibited operating zones, ramp rate limits, capacity limits and power balance constraints. In the proposed HPSO method, the best features of both EP and PSO are exploited, and it is capable of finding the most optimal solution for the non-linear optimization problems. For validating the proposed method, it has been tested on the standard three, six, fifteen and twenty unit test systems. The numerical results show that the proposed HPSO method is well suitable for solving non-linear economic dispatch problems, and it outperforms the EP, PSO and other modern metaheuristic optimization methods reported in the recent literatures.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2005.04a
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• pp.317-324
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• 2005

Korean shipyards have accumulated a great amount of data. But they do not have appropriate tools to utilize the data in practical works. Engineering data contains experts' experience and know-how In its own. It is very useful to extract knowledge or information from the accumulated existing data by using datamining technique. This paper treats an evolutionary computation method based on genetic programming (GP), which can be one of the components to realize datamining.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.5
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• pp.624-629
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• 1998

Evolutionary computation techniques can solve search problems using simulated evolution based on the ‘survival of the fittest’. Recently, the genetic programming (GP) which evolves computer programs using the genetic algorithm was introduced. In this paper, the genetic programming technique is used in order to design a rule based controller consisting of condition-action rules for an unknown system. No a priori knowledge about the structure of the controller is needed. Representation of a solution, functions and terminals in GP are analyzed, and a method of constructing a fuzzy logic controller using the obtained rule based controller is described. A simulation example using a nonlinear system shows the validity and efficiency of the proposed method.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.829-831
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• 1996

This paper is concerning on a study on the object-oriented programming and its application to maintenance scheduling. The concept of object-oriented programming enables us to modify and reuse software with much ease. By introducing object-oriented programming to maintenance scheduling, we can develop a hierarchical and reusable software in maintenance scheduling. The maintenance scheduling problem becoming more and more large and complex can be dealt with the concept of object-oriented technique and we hope this concept will give a reasonable solution. And evolutionary computation will be developed as a optimization technique.