Performance Evaluation of a Distributed Genetic Algorithm with Cellular Structures on Function Optimization Problems

2004 ◽  
pp. 1128-1135 ◽  
Author(s):  
Tadahiko Murata ◽  
Kenji Takada
Keyword(s):  
Genetic Algorithm ◽  
Performance Evaluation ◽  
Optimization Problems ◽  
Function Optimization ◽  
Cellular Structures ◽  
Distributed Genetic Algorithm

scholarly journals Hybrid Genetic Algorithm and Simulated Annealing for Function Optimization

2017 ◽  
Vol 1 (2) ◽  
pp. 82 ◽  
Author(s):  
Tirana Noor Fatyanosa ◽  
Andreas Nugroho Sihananto ◽  
Gusti Ahmad Fanshuri Alfarisy ◽  
M Shochibul Burhan ◽  
Wayan Firdaus Mahmudy
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Optimization Problems ◽  
Linear Optimization ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Function Optimization ◽  
Non Linear ◽  
Speed Up ◽  
Linear Problems

The optimization problems on real-world usually have non-linear characteristics. Solving non-linear problems is time-consuming, thus heuristic approaches usually are being used to speed up the solution’s searching. Among of the heuristic-based algorithms, Genetic Algorithm (GA) and Simulated Annealing (SA) are two among most popular. The GA is powerful to get a nearly optimal solution on the broad searching area while SA is useful to looking for a solution in the narrow searching area. This study is comparing performance between GA, SA, and three types of Hybrid GA-SA to solve some non-linear optimization cases. The study shows that Hybrid GA-SA can enhance GA and SA to provide a better result

Niche Genetic Algorithm Based on Sexual Reproduction and Multimodal Function Optimization Problem

2013 ◽  
pp. 8-16
Author(s):  
Yulong Tian ◽  
Tao Gao ◽  
Weifang Zhai ◽  
Yaying Hu ◽  
Xinfeng Li
Keyword(s):  
Genetic Algorithm ◽  
Sexual Reproduction ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Evolutionary Optimization ◽  
Function Optimization ◽  
Premature Convergence ◽  
Multimodal Functions ◽  
Multimodal Function Optimization ◽  
Multimodal Function

In this paper, a genetic algorithm with sexual reproduction and niche selection technology is proposed. Simple genetic algorithm has been successfully applied to many evolutionary optimization problems. But there is a problem of premature convergence for complex multimodal functions. To solve it, the frame and realization of niche genetic algorithm based on sexual reproduction are presented. Age and sexual structures are given to the individuals referring the sexual reproduction and “niche” phenomena, importing the niche selection technology. During age and sexual operators, different evolutionary parameters are given to the individuals with different age and sexual structures. As a result, this genetic algorithm can combat premature convergence and keep the diversity of population. The testing for Rastrigin function and Shubert function proves that the niche genetic algorithm based on sexual reproduction is effective.

Generalized Distributed Genetic Algorithm for Optimization Problems

1997 ◽  
Vol 4 (4) ◽  
pp. 276-289
Author(s):  
Alok Srivastava ◽  
Anup Kumar ◽  
Rakesh Pathak
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Distributed Genetic Algorithm

A Java-Based Package for Genetic Algorithm

2013 ◽  
Vol 717 ◽  
pp. 428-432
Author(s):  
Shan Ping Qiao ◽  
Bao Qiang Yan
Keyword(s):  
Genetic Algorithm ◽  
Programming Language ◽  
Computer Programming ◽  
Optimization Problems ◽  
Object Oriented ◽  
Function Optimization ◽  
Good Effect ◽  
The Future ◽  
Advanced Computer

Genetic algorithm is a very important and popular kind of algorithm of evolution computing. In order to use this algorithm better and platform-independently, this paper introduces an implement package which is coded in Java, an object-oriented and platform-independent advanced computer programming language, for genetic algorithm. This package includes several sub-packages. In each sub-package, there are some classes with different roles and functions. After the test, these classes can work properly and efficiently in together. The good effect has been received through using this algorithm in four function optimization problems. For the further goal, some studies even need to be carried out in the future.

A Novel Hybrid Genetic Algorithm for Unconstrained and Constrained Function Optimization

2021 ◽  
pp. 148-180
Author(s):  
Rajashree Mishra ◽  
Kedar Nath Das
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Hybrid Genetic Algorithm ◽  
Real Life ◽  
Function Optimization ◽  
Bacterial Foraging Optimization ◽  
E Coli ◽  
Application Problem ◽  
Active Interest ◽  
Nature Inspired Algorithms

During the past decade, academic and industrial communities are highly interested in evolutionary techniques for solving optimization problems. Genetic Algorithm (GA) has proved its robustness in solving all most all types of optimization problems. To improve the performance of GA, several modifications have already been done within GA. Recently GA has been hybridized with many other nature-inspired algorithms. As such Bacterial Foraging Optimization (BFO) is popular bio inspired algorithm based on the foraging behavior of E. coli bacteria. Many researchers took active interest in hybridizing GA with BFO. Motivated by such popular hybridization of GA, an attempt has been made in this chapter to hybridize GA with BFO in a novel fashion. The Chemo-taxis step of BFO plays a major role in BFO. So an attempt has been made to hybridize Chemo-tactic step with GA cycle and the algorithm is named as Chemo-inspired Genetic Algorithm (CGA). It has been applied on benchmark functions and real life application problem to prove its efficacy.

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