• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.28 no.3
• /
• pp.149-155
• /
• 2005

In this paper, we optimize simulation model of a manufacturing system using the real-coded genetic algorithm. Because the manufacturing system expressed by simulation model has stochastic process, the objective functions such as the throughput of a manufacturing system or the resource utilization are not optimized by simulation itself. So, in order to solve it, we apply optimization methods such as a genetic algorithm to simulation method. Especially, the genetic algorithm is known to more effective method than other methods to find global optimum, because the genetic algorithm uses entity pools to find the optimum. In this study, therefore, we apply the real-coded genetic algorithm to simulation optimization of a manufacturing system, which is known to more effective method than the binary-coded genetic algorithm when we optimize the constraint problems. We use the reproduction operator of the applied real-coded genetic algorithm as technique of the remainder stochastic sample with replacement and the crossover operator as the technique of simple crossover. Also, we use the mutation operator as the technique of the dynamic mutation that configures the searching area with generations.

• Journal of Computing Science and Engineering
• /
• v.10 no.4
• /
• pp.111-117
• /
• 2016

An accurate approach for diagnosis of attention deficit hyperactivity disorder (ADHD) is presented in this paper. The presented technique efficiently classifies three subtypes of ADHD (ADHD-C, ADHD-H, ADHD-I) and typically developing control (TDC) by using only structural magnetic resonance imaging (MRI). The research examines structural MRI of the hippocampus from the ADHD-200 database. Each available MRI has been processed by a region-of-interest (ROI) to build a set of features for further analysis. The presented ADHD diagnostic approach unifies feature selection and classification techniques. The feature selection technique based on the proposed binary-coded genetic algorithm searches for an optimal subset of features extracted from the hippocampus. The classification technique uses a chosen optimal subset of features for accurate classification of three subtypes of ADHD and TDC. In this study, the famous Extreme Learning Machine is used as a classification technique. Experimental results clearly indicate that the presented BCGA-ELM (binary-coded genetic algorithm coupled with Extreme Learning Machine) efficiently classifies TDC and three subtypes of ADHD and outperforms existing techniques.

• Journal of Ship and Ocean Technology
• /
• v.9 no.4
• /
• pp.35-46
• /
• 2005

The performance of optimisation methods, based on penalty functions, is highly problem- dependent and many methods require additional tuning of some variables. This additional tuning is the influences of penalty coefficient, which depend strongly on the degree of constraint violation. Moreover, Binary-coded Genetic Algorithm (BGA) meets certain difficulties when dealing with continuous and/or discrete search spaces with large dimensions. With the above reasons, Real-coded Micro-Genetic Algorithm (R$\mu$GA) is proposed to find the global optimum of continuous and/or discrete nonlinear constrained engineering problems without handling any of penalty functions. R$\mu$GA can help in avoiding the premature convergence and search for global solution-spaces, because of its wide spread applicability, global perspective and inherent parallelism. The proposed R$\mu$GA approach has been demonstrated by solving three different engineering design problems. From the simulation results, it has been concluded that R$\mu$GA is an effective global optimisation tool for solving continuous and/or discrete nonlinear constrained real­world optimisation problems.

• Journal of Power System Engineering
• /
• v.7 no.3
• /
• pp.29-34
• /
• 2003

Genetic algorithm is a optimization technique based on the mechanics of natural selection and natural genetics. Global optimum solution can be obtained efficiently by operations of reproduction, crossover and mutation in genetic algorithm. The authors developed a computer program which can optimize marine propulsion shafting by using binary-coded genetic algorithm and modal analysis method. In order to confirm the effectiveness of the developed computer program, we apply the program to a optimum design problem which is to obtain optimum diameters of intermediate shaft and propeller shaft in marine propulsion shafting. Objective function is to minimize total mass of shafts and constraints are that torsional vibration stresses of shafts in marine propulsion shafting can not exceed the permissible torsional vibration stresses of the ship classification society. The computational results by the program were compared with those of conventional design technique.

• Structural Engineering and Mechanics
• /
• v.52 no.6
• /
• pp.1099-1120
• /
• 2014

In this paper, reliability-based design optimization (RBDO) of structures is addressed. For this purpose, the global search and optimization capabilities of genetic algorithm (GA) are combined with the efficiency and reasonable accuracy of an advanced moment-based finite element reliability method. For performing RBDO, three variants of GA including a real-coded, a binary-coded and an improved binary-coded GA are developed. In these methods, GA performs (finite element) reliability analyses to evaluate reliability constraints. For truss structures which include finite element modeling, reliability constraints are evaluated using finite element reliability analysis. Response sensitivity required for finite element reliability analysis is obtained by direct differentiation method (DDM) rather than finite difference method (FDM). The proposed methods are examined within four standard test examples and real-world design problems. The results illustrate the superiority and efficiency of the improved binary-coded GA. Results also illustrate that DDM significantly reduces the computational cost and improves the efficiency of the optimization procedure.

• Journal of Korea Game Society
• /
• v.17 no.5
• /
• pp.123-132
• /
• 2017

The path-finding algorithm refers to an algorithm for navigating the route order from the current position to the destination in a virtual world in a game. The conventional path-finding algorithm performs graph search based on cost such as A-Star and Dijkstra. A-Star and Dijkstra require movable node and edge data in the world map, so it is difficult to apply online games with lots of map data. In this paper, we provide a Heuristic-based Genetic Algorithm Path-finding(HGAP) using Genetic Algorithm(GA). Genetic Algorithm is a path-finding algorithm applicable to game with variable environment and lots of map data. It seek solutions through mating, crossing, mutation and evolutionary operations without the map data. The proposed algorithm is based on Binary-Coded Genetic Algorithm and searches for a path by performing a heuristic operation that estimates a path to a destination to arrive at a destination more quickly.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.59 no.1
• /
• pp.184-191
• /
• 2010

Many researchers have been considering genetic algorithms to system optimization problems. Especially, real-coded genetic algorithms are very effective techniques because they are simpler in coding procedures than binary-coded genetic algorithms and can reduce extra works that increase the length of chromosome for wide search space. Thus, this paper presents a fuzzy system design technique to improve the performance of the fuzzy system. The proposed system consists of two procedures. The primary tuning procedure coarsely tunes fuzzy sets of the system using the k-means clustering algorithm of which the structure is very simple, and then the secondary tuning procedure finely tunes the fuzzy sets using enhanced real-coded genetic algorithms based on the primary procedure. In addition, this paper constructs multiple fuzzy systems using a data preprocessing procedure which is contrived for reflecting various characteristics of nonlinear data. Finally, the proposed fuzzy system is applied to the field of time series prediction and the effectiveness of the proposed techniques are verified by simulations of typical time series examples.

• Journal of Navigation and Port Research
• /
• v.30 no.2
• /
• pp.137-143
• /
• 2006

The optimum grillage design belongs to nonlinear constrained optimization problem. The determination of beam scantlings for the grillage structure is a very crucial matter out of whole structural design process. The performance of optimization methods, based on penalty functions, is highly problem-dependent and many methods require additional tuning of some variables. This additional tuning is the influences of penalty coefficient, which depend strongly on the degree of constraint violation. Moreover, Binary-coded Genetic Algorithm (BGA) meets certain difficulties when dealing with continuous and/or discrete search spaces with large dimensions. With the above reasons, Real-coded Micro-Genetic Algorithm ($R{\mu}GA$) is proposed to find the optimum beam scantlings of the grillage structure without handling any of penalty functions. $R{\mu}GA$ can help in avoiding the premature convergence and search for global solution-spaces, because of its wide spread applicability, global perspective and inherent parallelism. Direct stiffness method is used as a numerical tool for the grillage analysis. In optimization study to find minimum weight, sensitivity study is carried out with varying beam configurations. From the simulation results, it has been concluded that the proposed $R{\mu}GA$ is an effective optimization tool for solving continuous and/or discrete nonlinear real-world optimization problems.

• Proceedings of the IEEK Conference
• /
• 1998.06a
• /
• pp.905-908
• /
• 1998

In this paper, we propose an identification method for nonlinear systems. In order to identify the nonlinear system parameters, we are represented the linearization from the nonlinear system, and use a genetic algorithm(GA). The parameters are coded into binary string and searched by GA. The simulation results show the effectiveness of the proposed approach.

• Journal of Institute of Control, Robotics and Systems
• /
• v.9 no.2
• /
• pp.168-175
• /
• 2003

A new algorithm based on Genetic Algorithms is proposed f3r solving process optimization problems formulated in MINLP, GDP and hybrid MINLP/GDP. This work is focused especially on the design of the Genetic Algorithm suitable to handle disjunctive programming with the same level of MINLP handling capability. Hybridization with the Simulated Annealing is experimented and many heuristics are adopted. Real and binary coded Genetic Algorithm initiates the global search in the entire search space and at every stage Simulated Annealing makes the candidates to climb up the local hills. Multi-Niche Crowding method is adopted as the multimodal function optimization technique. and the adaptation of probabilistic parameters and dynamic penalty systems are also implemented. New strategies to take the logical variables and constraints into consideration are proposed, as well. Various test problems selected from many fields of process systems engineering are tried and satisfactory results are obtained.