Parallel Model of Evolutionary Computing Based on Genetic Algorithm

2010 ◽  
Author(s):  
Xiaogang Wang ◽  
Yan Bai ◽  
Yue Li
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Computing ◽  
Parallel Model

Mathematical Optimization by Using Particle Swarm Optimization, Genetic Algorithm, and Differential Evolution and Its Similarities

2021 ◽  
pp. 1-34
Author(s):  
Shailendra Aote ◽  
Mukesh M. Raghuwanshi
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Mathematical Optimization ◽  
Evolutionary Computing ◽  
Swarm Optimization

To solve the problems of optimization, various methods are provided in different domain. Evolutionary computing (EC) is one of the methods to solve these problems. Mostly used EC techniques are available like Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Differential Evolution (DE). These techniques have different working structure but the inner working structure is same. Different names and formulae are given for different task but ultimately all do the same. Here we tried to find out the similarities among these techniques and give the working structure in each step. All the steps are provided with proper example and code written in MATLAB, for better understanding. Here we started our discussion with introduction about optimization and solution to optimization problems by PSO, GA and DE. Finally, we have given brief comparison of these.

Semantic-Based Video Scene Retrieval Using Evolutionary Computing

2011 ◽  
pp. 273-291 ◽  
Author(s):  
Hun-Woo Yoo
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Computing ◽  
Similarity Function ◽  
Initial Population ◽  
Next Generation ◽  
Retrieval Method ◽  
Interactive Genetic Algorithm ◽  
Feature Vectors ◽  
Video Scene

A new emotion-based video scene retrieval method is proposed in this chapter. Five video features extracted from a video are represented in a genetic chromosome and target videos that user has in mind are retrieved by the interactive genetic algorithm through the feedback iteration. After the proposed algorithm selects the videos that contain the corresponding emotion from the initial population of videos, the feature vectors from them are regarded as chromosomes, and a genetic crossover is applied to those feature vectors. Next, new chromosomes after crossover and feature vectors in the database videos are compared based on a similarity function to obtain the most similar videos as solutions of the next generation. By iterating this process, a new population of videos that a user has in mind are retrieved. In order to show the validity of the proposed method, six example categories of “action,” “excitement,” “suspense,” “quietness,” “relaxation,” and “happiness” are used as emotions for experiments. This method of retrieval shows 70% of effectiveness on the average over 300 commercial videos.

scholarly journals Evolutionary Computing based Neuron-Computational Model for Microstrip Patch Antenna Design Optimization

2021 ◽  
Vol 13 (03) ◽  
pp. 15-40
Author(s):  
Rohini Saxena ◽  
Mukesh Kumar ◽  
Shadman Aslam
Keyword(s):  
Genetic Algorithm ◽  
Patch Antenna ◽  
Evolutionary Computing ◽  
Microstrip Patch Antenna ◽  
Ultra Wideband ◽  
Antenna Design ◽  
Design Parameters ◽  
Adaptive Genetic Algorithm ◽  
Microstrip Patch ◽  
Depth Analysis

In this paper, a novel Evolutionary Computing named Adaptive Genetic Algorithm (AGA) based ANN model is developed for rectangular MPA (Microstrip patch antenna). Considering at-hand and Nextgeneration Ultra wideband application demands, the emphasis has been made on retaining optimal lowcost design with desired cut-off frequency. The proposed method employs multiple sets of theoreticallydriven training instances or patch antenna design parameters which have been processed for normalization and sub-sampling to achieve a justifiable and reliable sample size for further design parameter prediction. Procedurally, the input design parameters were processed for normalization followed by sub-sampling to give rise to a sufficient set of inputs to perform knowledge-driven (designparameter) prediction. Considering limitations of the major at-hand machine learning methods which often undergo local minima and convergence while training, we designed a state-of-art new Adaptive Genetic Algorithm based neuro-computing model (AGA-ANN), which helped to predict the set of optimal design parameters for rectangular microstrip patch antenna. The predicted patch antenna length and width values were later used for verification which achieved the expected frequency. The depth analysis revealed that a rectangular patch antenna with width 14.78 mm, length 11.08mm, feed-line 50 Ω can achieve the cut-off frequency of 8.273 GHz, which can be of great significance for numerous UWB applications.

Mathematical Optimization by Using Particle Swarm Optimization, Genetic Algorithm, and Differential Evolution and Its Similarities

2017 ◽  
pp. 325-358
Author(s):  
Shailendra Aote ◽  
Mukesh M. Raghuwanshi
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Mathematical Optimization ◽  
Evolutionary Computing ◽  
Swarm Optimization

To solve the problems of optimization, various methods are provided in different domain. Evolutionary computing (EC) is one of the methods to solve these problems. Mostly used EC techniques are available like Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Differential Evolution (DE). These techniques have different working structure but the inner working structure is same. Different names and formulae are given for different task but ultimately all do the same. Here we tried to find out the similarities among these techniques and give the working structure in each step. All the steps are provided with proper example and code written in MATLAB, for better understanding. Here we started our discussion with introduction about optimization and solution to optimization problems by PSO, GA and DE. Finally, we have given brief comparison of these.

scholarly journals Parallel genetic algorithms with migration for the hybrid flow shop scheduling problem

2006 ◽  
Vol 2006 ◽  
pp. 1-17 ◽  
Author(s):  
K. Belkadi ◽  
M. Gourgand ◽  
M. Benyettou
Keyword(s):  
Genetic Algorithm ◽  
Flow Shop ◽  
Space Structure ◽  
Flow Shop Scheduling ◽  
Computational Time ◽  
Hybrid Flow Shop ◽  
Parallel Model ◽  
Scheduling Problems ◽  
Parallel Genetic Algorithm ◽  
Hybrid Flow Shop Scheduling

This paper addresses scheduling problems in hybrid flow shop-like systems with a migration parallel genetic algorithm (PGA_MIG). This parallel genetic algorithm model allows genetic diversity by the application of selection and reproduction mechanisms nearer to nature. The space structure of the population is modified by dividing it into disjoined subpopulations. From time to time, individuals are exchanged between the different subpopulations (migration). Influence of parameters and dedicated strategies are studied. These parameters are the number of independent subpopulations, the interconnection topology between subpopulations, the choice/replacement strategy of the migrant individuals, and the migration frequency. A comparison between the sequential and parallel version of genetic algorithm (GA) is provided. This comparison relates to the quality of the solution and the execution time of the two versions. The efficiency of the parallel model highly depends on the parameters and especially on the migration frequency. In the same way this parallel model gives a significant improvement of computational time if it is implemented on a parallel architecture which offers an acceptable number of processors (as many processors as subpopulations).

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