Maintaining population diversity in a genetic algorithm: an example in developing control schemes for semiconductor manufacturing

1997 ◽  
Author(s):  
Edward A. Rietman
Keyword(s):  
Genetic Algorithm ◽  
Semiconductor Manufacturing ◽  
Population Diversity ◽  
Control Schemes

scholarly journals An Enhanced Genetic Algorithm for Parameter Estimation of Sinusoidal Signals

2020 ◽  
Vol 10 (15) ◽  
pp. 5110
Author(s):  
Chao Jiang ◽  
Pruthvi Serrao ◽  
Mingjie Liu ◽  
Chongdu Cho
Keyword(s):  
Genetic Algorithm ◽  
Fundamental Problem ◽  
Cloud Model ◽  
Real Life ◽  
Cuckoo Search ◽  
Population Diversity ◽  
Adaptive Mutation ◽  
Parameter Estimations ◽  
Mutation Strategy ◽  
Sinusoidal Signals

Estimating the parameters of sinusoidal signals is a fundamental problem in signal processing and in time-series analysis. Although various genetic algorithms and their hybrids have been introduced to the field, the problems pertaining to complex implementation, premature convergence, and accuracy are still unsolved. To overcome these drawbacks, an enhanced genetic algorithm (EGA) based on biological evolutionary and mathematical ecological theory is originally proposed in this study; wherein a prejudice-free selection mechanism, a two-step crossover (TSC), and an adaptive mutation strategy are designed to preserve population diversity and to maintain a synergy between convergence and search ability. In order to validate the performance, benchmark function-based studies are conducted, and the results are compared with that of the standard genetic algorithm (SGA), the particle swarm optimization (PSO), the cuckoo search (CS), and the cloud model-based genetic algorithm (CMGA). The results reveal that the proposed method outperforms the others in terms of accuracy, convergence speed, and robustness against noise. Finally, parameter estimations of real-life sinusoidal signals are performed, validating the superiority and effectiveness of the proposed method.

Multi-Operator Genetic Algorithm for Dynamic Optimization Problems

2017 ◽  
Vol 6 (3) ◽  
pp. 139
Author(s):  
Al-khafaji Amen
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Dynamic Optimization ◽  
Optimization Problems ◽  
State Of The Art ◽  
Population Diversity ◽  
Dynamic Optimization Problems ◽  
Uncertain Environments ◽  
Convergence Problem ◽  
Royal Road

<span lang="EN-US">Maintaining population diversity is the most notable challenge in solving dynamic optimization problems (DOPs). Therefore, the objective of an efficient dynamic optimization algorithm is to track the optimum in these uncertain environments, and to locate the best solution. In this work, we propose a framework that is based on multi operators embedded in genetic algorithms (GA) and these operators are heuristic and arithmetic crossovers operators. The rationale behind this is to address the convergence problem and to maintain the diversity. The performance of the proposed framework is tested on the well-known dynamic optimization functions i.e., OneMax, Plateau, Royal Road and Deceptive. Empirical results show the superiority of the proposed algorithm when compared to state-of-the-art algorithms from the literature.</span>

scholarly journals Genetic Algorithm for Optimizing Routing Design and Fleet Allocation of Freeway Service Overlapping Patrol

2018 ◽  
Vol 10 (11) ◽  
pp. 4120 ◽  
Author(s):  
Xiuqiao Sun ◽  
Jian Wang ◽  
Weitiao Wu ◽  
Wenjia Liu
Keyword(s):  
Genetic Algorithm ◽  
Population Diversity ◽  
Decision Makers ◽  
Incident Response ◽  
Matrix Encoding ◽  
Routing Design ◽  
Speed Up ◽  
Fleet Allocation ◽  
Patrol Routing ◽  
Elitist Strategy

The freeway service patrol problem involves patrol routing design and fleet allocation on freeways that would help transportation agency decision-makers when developing a freeway service patrols program and/or altering existing route coverage and fleet allocation. Based on the actual patrol process, our model presents an overlapping patrol model and addresses patrol routing design and fleet allocation in a single integrated model. The objective is to minimize the overall average incident response time. Two strategies—overlapping patrol and non-overlapping patrol—are compared in our paper. Matrix encoding is applied in the genetic algorithm (GA), and to maintain population diversity and avoid premature convergence, a niche strategy is incorporated into the traditional genetic algorithm. Meanwhile, an elitist strategy is employed to speed up the convergence. Using numerical experiments conducted based on data from the Sioux Falls network, we clearly show that: overlapping patrol strategy is superior to non-overlapping patrol strategy; the GA outperforms the simulated annealing (SA) algorithm; and the computational efficiency can be improved when LINGO software is used to solve the problem of fleet allocation.

scholarly journals Genetic Algorithm for Job Scheduling with Maintenance Consideration in Semiconductor Manufacturing Process

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Seungchul Lee ◽  
Jun Ni
Keyword(s):  
Genetic Algorithm ◽  
Semiconductor Manufacturing ◽  
Job Scheduling ◽  
Optimization Methods ◽  
Optimization Method ◽  
Scheduling Problems ◽  
Simulation Based ◽  
Sequencing Problems ◽  
Maintenance Activities ◽  
Single Cluster

This paper presents wafer sequencing problems considering perceived chamber conditions and maintenance activities in a single cluster tool through the simulation-based optimization method. We develop optimization methods which would lead to the best wafer release policy in the chamber tool to maximize the overall yield of the wafers in semiconductor manufacturing system. Since chamber degradation will jeopardize wafer yields, chamber maintenance is taken into account for the wafer sequence decision-making process. Furthermore, genetic algorithm is modified for solving the scheduling problems in this paper. As results, it has been shown that job scheduling has to be managed based on the chamber degradation condition and maintenance activities to maximize overall wafer yield.

Design of GA-Based Control for Electrical Servo Drive

2011 ◽  
Vol 201-203 ◽  
pp. 2375-2378
Author(s):  
Kuo Ho Su ◽  
Feng Hsiang Hsiao
Keyword(s):  
Genetic Algorithm ◽  
Gradient Descent ◽  
Nonlinear Dynamical System ◽  
Servo Drive ◽  
Control Effort ◽  
Nonlinear Dynamical ◽  
Supervisory Controller ◽  
Control Scheme ◽  
Control Schemes ◽  
System States

An alternative control scheme including a directional genetic algorithm controller (DGAC) and a supervisory controller is developed to control the position of an electrical servo drive in this study. In the DGAC design, the spirit of gradient descent training is embedded in genetic algorithm (GA) to construct a main controller to search optimum control effort under possible occurrence of uncertainties. In order to ensure the system states around a defined bound region, a supervisory controller, which is derived in the sense of Lyapunov stability theorem, is added to adjust the control effort. Compared with enunciated GA control methods, the proposed control scheme possesses some salient advantages of simple framework, fewer executing time and good self-organizing properties even for nonlinear dynamical system. The effectiveness is demonstrated by simulation results, and its advantages are indicated in comparison with other GA control schemes for a field-oriented control induction motor drive.

Precise Angle Control of Electromechanical Actuator with Fuzzy PID and Genetic Algorithm

2013 ◽  
Vol 300-301 ◽  
pp. 1479-1485
Author(s):  
Ye Bing Cui ◽  
Jian Zheng ◽  
Yu Tao Ju ◽  
Jing Xu
Keyword(s):  
Genetic Algorithm ◽  
Pid Controller ◽  
Fuzzy Pid ◽  
Space Applications ◽  
Electromechanical Actuators ◽  
Electromechanical Actuator ◽  
Brushless Dc Motors ◽  
Brushless Dc ◽  
Dc Motors ◽  
Control Schemes

With the development of the new kinds of permanent magnetic materials and the tech of drive circuits, more and more electromechanical actuators have been used in the space applications, such as tactical missiles, smart UAV and so on. This study presents an electromechanical actuator actuated by four Brushless DC motors (BLDC) driven rudder wings .Two different control schemes are implemented to regulate the output angle of the EMA rudder wings. Namely a fuzzy logic PID controller （Fuzzy-PID）and a genetic algorithm optimized PID (GA-PID) controller. The feasibility of the two controllers is evaluated both numerically and experimentally, it is shown that Fuzzy-PID leads to a loss of control in high frequency conditions, while, GA-PID can ensure the precise angle control and an accurate tracking performance.

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