• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.223-226
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• 1997

Usually, the Evolutionary Algorithms(EAs) are considered more efficient for optimal, system design because EAs can provide higher opportunity for obtaining the global optimal solution. This paper presents a mechanism of co-evolution consists of the two genetic algorithms(GAs). This mechanism includes host populations and parasite populations. These two populations are closely related to each other, and the parasite populations plays an important role of searching for useful schema in host populations. Host population represented by feedforward neural network and the result of co-evolution we will find the optimal structure of the neural network. We used the genetic algorithm that search the structure of the feedforward neural network, and evolution strategies which train the weight of neuron, and optimize the net structure. The validity and effectiveness of the proposed method is exemplified on the stabilization and position control of the inverted-pendulum system.

• Journal of Microbiology and Biotechnology
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• v.30 no.6
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• pp.793-803
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• 2020

Adaptive laboratory evolution (ALE) is an evolutionary engineering approach in artificial conditions that improves organisms through the imitation of natural evolution. Due to the development of multi-level omics technologies in recent decades, ALE can be performed for various purposes at the laboratory level. This review delineates the basics of the experimental design of ALE based on several ALE studies of industrial microbial strains and updates current strategies combined with progressed metabolic engineering, in silico modeling and automation to maximize the evolution efficiency. Moreover, the review sheds light on the applicability of ALE as a strain development approach that complies with non-recombinant preferences in various food industries. Overall, recent progress in the utilization of ALE for strain development leading to successful industrialization is discussed.

• Smart Structures and Systems
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• v.14 no.1
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• pp.17-37
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• 2014

In this study, four widely accepted and used variants of Evolution Strategies (ES) are adapted and applied to the output-error state-space identification problem. The selection of ES is justified by prior strong indication of superior performance to similar problems, over alternatives like Genetic Algorithms (GA) or Evolutionary Programming (EP). The ES variants that are being tested are (i) the (1+1)-ES, (ii) the $({\mu}/{\rho}+{\lambda})-{\sigma}$-SA-ES, (iii) the $({\mu}_I,{\lambda})-{\sigma}$-SA-ES, and (iv) the (${\mu}_w,{\lambda}$)-CMA-ES. The study is based on a six-degree-of-freedom (DOF) structural model of a shear building that is characterized by light damping (up to 5%). The envisaged analysis is taking place through Monte Carlo experiments under two different excitation types (stationary / non-stationary) and the applied ES are assessed in terms of (i) accurate modal parameters extraction, (ii) statistical consistency, (iii) performance under noise-corrupted data, and (iv) performance under non-stationary data. The results of this suggest that ES are indeed competitive alternatives in the non-linear state-space estimation problem and deserve further attention.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.05a
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• pp.77-81
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• 2003

SDM (Structural Dynamics Modification) is a tool to improve dynamic characteristics of a structure, more specifically of a base structure, by adding or deleting auxiliary (modifying) structures. In this paper, the goal of the optimal SDM is set to maximize the natural frequency of a base plate structure by attaching serially-connected beam stiffeners. The design variables are chosen as positions of the attaching beam stiffeners, where the number of stiffeners is considered as a design space. The problem of non-matching interface nodes between the base plate and beam stiffeners is solved by using localized Lagrange multipliers, which act to glue the two structures with non-matching interface nodes. As fer the cases of non-matching interface nodes problem, the governing equation of motion of a structure can be considered from the viewpoint of a topological modification, which involves the change of the number of structural members and DOFs. Consequently, the eigenpairs of the beam-stiffened plate structure are obtained by using an eigen reanalysis technique of topological modifications. Evolution Strategies (ES), which is a probabilistic population-based optimization technique that mimics the principles from biological evolution in nature, is utilized as a mean for the optimization.

• Journal of the Society of Naval Architects of Korea
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• v.40 no.4
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• pp.53-58
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• 2003

Design of the corrugated watertight bulkhead for a bulk carrier is principally determined by the permissible limit of Classification requirements. As the weight of upper and lower stool has considerable portion of the total weight of the transverse bulkhead, optimum design including the stool geometry and size will play an important role on economic shipbuilding. The purpose of this study is focused on the minimization of steel weight using the design variables, which are the shape and the size of the corrugation as well as the upper and lower stools. Discrete variables are used as design variables for the practical design. In this study, the evolution strategies (ES), which can highly improve the possibility of leaching the global minimum point, are selected as an optimization method. Usefulness of this study is verified by comparison with the proven type ship design. As objective function, total weight of the transverse bulkhead including the upper and lower stools is used.

• Structural Monitoring and Maintenance
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• v.1 no.4
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• pp.427-449
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• 2014

A hybrid optimization method for the identification of state-space models is presented in this study. Hybridization is succeeded by combining the advantages of deterministic and stochastic algorithms in a superior scheme that promises faster convergence rate and reliability in the search for the global optimum. The proposed hybrid algorithm is developed by replacing the original stochastic mutation operator of Evolution Strategies (ES) by the Levenberg-Marquardt (LM) quasi-Newton algorithm. This substitution results in a scheme where the entire population cloud is involved in the search for the global optimum, while single individuals are involved in the local search, undertaken by the LM method. The novel hybrid identification framework is assessed through the Monte Carlo analysis of a simulated system and an experimental case study on a shear frame structure. Comparisons to subspace identification, as well as to conventional, self-adaptive ES provide significant indication of superior performance.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.302-305
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• 1996

This paper presents a visual servoing combined by Neural Network with optimal structure and predictive control for robotic manipulators to tracking or grasping of the moving object. Using the four feature image information from CCD camera attached to end-effector of RV-M2 robot manipulator having 5 dof, we want to predict the updated position of the object. The Kalman filter is used to estimate the motion parameters, namely the state vector of the moving object in successive image frames, and using the multi layer feedforward neural network that permits the connection of other layers, evolutionary programming(EP) that search the structure and weight of the neural network, and evolution strategies(ES) which training the weight of neuron, we optimized the net structure of control scheme. The validity and effectiveness of the proposed control scheme and predictive control of moving object will be verified by computer simulation.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.1135-1136
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• 2008

Error correcting codes (ECC) are widely used in all types of memory in industry, including caches and embedded memory. The focus in this paper is on studying of power consumption in memory ECCs circuitry that provides single error correcting and double error detecting (SEC-DED) designed by various evolution strategies. The methods are applied to two commonly used SEC-DED codes: Hamming and odd column weight Hsiao codes. Finally, we conduct some simulations to show the performance of the various methods.

• 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.

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2193-2196
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• 1998

Cellular automata are dynamical systems in which space and time are discrete, where each cell has a finite number of states and updates its states by interactive rules among the cell-neighborhood. From the characteristics of self-reproduction and self- organization, it is possible to create a neural network which has the specific patterns or structures dynamically. CAM-Brain is a kind of such neural network system which evolves its structure by adopting evolutionary computations like genetic algorithms (GA). In this paper, we suggest the evolution strategies for the structure of neural networks based on cellular automata.