scholarly journals Elite Correlation Selection Operator : A new selection operator to improve the diversity in genetic algorithm

2003 ◽  
Vol 2003 (0) ◽  
pp. 142-147
Author(s):  
Minoru ITO ◽  
Masanori SUGISAKA
Keyword(s):  
Genetic Algorithm ◽  
Selection Operator

scholarly journals Generalized solution for inverse kinematics problem of a robot using hybrid genetic algorithms

2018 ◽  
Vol 7 (4.6) ◽  
pp. 250
Author(s):  
Pavan Kumar K ◽  
Murali Mohan J ◽  
Srikanth D
Keyword(s):  
Genetic Algorithm ◽  
Inverse Kinematics ◽  
Dynamic Control ◽  
Hybrid Genetic Algorithm ◽  
Search Space ◽  
Generalized Solution ◽  
Inverse Kinematics Problem ◽  
Tournament Selection ◽  
Selection Operator ◽  
Forward Kinematic

The robot control consists of kinematic control and dynamic control. Control methods of the robot involve forward kinematics and inverse kinematics (IK). In Inverse kinematics the joint angles are found for a given position and orientation of the end effector. Inverse kinematics is a nonlinear problem and has multiple solutions. This computation is required to control the robot arms. A Genetic Algorithm (GA) and Hybrid genetic algorithm (HGA) (Genetic Algorithm in conjunction with Nelder-Mead technique) are proposed for solving the inverse kinematics of a robotic arm. HGA introduces two concepts exploration, exploitation. In an exploration phase, the GA identifies the good areas in entire search space and then exploitation phase is performed inside these areas by using Nelder- mead technique Binary Simulated Crossover and niching strategy for binary tournament selection operator is used. Proposed algorithms can be used on any type of manipulator and the only requirement is the forward kinematic equations, which are easily obtained. As a case study inverse kinematics of a Two Link Elbow Manipulator and PUMA manipulator are solved using GA and HGA in MATLAB. The algorithm is able to find all solutions without any error  

scholarly journals Using a Goal-Switching Selection Operator in Multi-Objective Genetic Algorithm Optimization Problems

2013 ◽  
Author(s):  
Daniel Shaefer ◽  
Scott Ferguson
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Pareto Frontier ◽  
Product Line ◽  
Algorithm Optimization ◽  
Nsga Ii ◽  
Solution Quality ◽  
Design Variables ◽  
Mp3 Player ◽  
Selection Operator

This paper demonstrates how solution quality for multiobjective optimization problems can be improved by altering the selection phase of a multiobjective genetic algorithm. Rather than the traditional roulette selection used in algorithms like NSGA-II, this paper adds a goal switching technique to the selection operator. Goal switching in this context represents the rotation of the selection operator among a problem’s various objective functions to increase search diversity. This rotation can be specified over a set period of generations, evaluations, CPU time, or other factors defined by the designer. This technique is tested using a set period of generations before switching occurs, with only one objective considered at a time. Two test cases are explored, the first as identified in the Congress on Evolutionary Computation (CEC) 2009 special session and the second a case study concerning the market-driven design of a MP3 player product line. These problems were chosen because the first test case’s Pareto frontier is continuous and concave while being relatively easy to find. The second Pareto frontier is more difficult to obtain and the problem’s design space is significantly more complex. Selection operators of roulette and roulette with goal switching were tested with 3 to 7 design variables for the CEC 09 problem, and 81 design variables for the MP3 player problem. Results show that goal switching improves the number of Pareto frontier points found and can also lead to improvements in hypervolume and/or mean time to convergence.

The System of Spindle Optimization Design Based on GA

2012 ◽  
Vol 466-467 ◽  
pp. 773-777 ◽  
Author(s):  
Peng Jia Wang ◽  
Chen Guang Guo ◽  
Yong Xian Liu ◽  
Zhong Qi Sheng
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Dynamic Property ◽  
Design Variable ◽  
Optimization Design ◽  
Search Space ◽  
Mutation Operator ◽  
Crossover Operator ◽  
Selection Operator ◽  
The Mathematical Model

Aiming at the optimization design of spindle, this paper introduces deflection constraint, strength constraint, corner constraint, cutting force constraint, the limit of torsional deflection, boundary constraint of design variable, dynamic property constraint , realizes the expression of the mathematical model of the spindle optimization design. Through the introduction of the real number code rule, the selection operator is built by adopting the optimum maintaining tactics and proportional selection, the crossover operator is built by using the method of arithmetic crossover and the mutation operator is built by using the method of uniform mutation. In the platform of VC++, the system of spindle optimization design based on GA is built. The analysis of the example shows that using the genetic algorithm to optimize the spindle can ensure the convergence of the optimization course, expand the search space, and the effect of optimization is obvious.

Research on the Genetic Algorithm Simulating Human Reproduction Mode and its Blending Application with Neural Network

2012 ◽  
Vol 532-533 ◽  
pp. 1785-1789
Author(s):  
Tai Shan Yan
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Optimal Solution ◽  
Bp Algorithm ◽  
Human Reproduction ◽  
Genetic Operators ◽  
Reproduction Mode ◽  
Evolutionary Neural Network ◽  
Selection Operator

In this study, a genetic algorithm simulating human reproduction mode (HRGA) is proposed. The genetic operators of HRGA include selection operator, help operator, crossover operator and mutation operator. The sex feature, age feature and consanguinity feature of genetic individuals are considered. Two individuals with opposite sex can reproduce the next generation if they are distant consanguinity individuals and their age is allowable. Based on this genetic algorithm, an improved evolutionary neural network algorithm named HRGA-BP algorithm is formed. In HRGA-BP algorithm, HRGA is used firstly to evolve and design the structure, the initial weights and thresholds, the training ratio and momentum factor of neural network roundly. Then, training samples are used to search for the optimal solution by the evolutionary neural network. HRGA-BP algorithm is used in motor fault diagnosis. The illustrational results show that HRGA-BP algorithm is better than traditional neural network algorithms in both speed and precision of convergence, and its validity in fault diagnosis is proved.

Research on Improved Genetic Algorithm Solving Flexible Job-Shop Problem

2012 ◽  
Vol 479-481 ◽  
pp. 1918-1921 ◽  
Author(s):  
Min Shuo Li ◽  
Ming Hai Yao
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Flexible Job Shop ◽  
Improved Genetic Algorithm ◽  
Total Delay ◽  
Retention Policy ◽  
Selection Operator ◽  
Flexible Job Shop Problem ◽  
Random Method

Based on the analyzing of the characteristic of the flexible job-shop scheduling problem (FJSP), we proposed an improved genetic algorithm. To consider the max finish-time, total delay-time, keeping workload balance among the machines, a new selection operator is proposed, which combines random method, proportion-based selection method with elitist retention policy. The improved genetic algorithm using the proposed selection operator is tested on some standard instances. The experimental results validate the effectiveness of the proposed algorithm.

A New Combinational Selection Operator in Genetic Algorithm

2011 ◽  
Author(s):  
Marjan Kuchaki Rafsanjani ◽  
Sadegh Eskandari ◽  
Theodore E. Simos ◽  
George Psihoyios ◽  
Ch. Tsitouras ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Selection Operator
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