On the convergence rate of the elitist genetic algorithm based on mutation probability

The study on convergence of GA is always one of the most important theoretical issues. This paper analyses the sufficient condition which guarantees the convergence of GA. Via analyzing the convergence rate of GA, the average computational complexity can be implied and the optimization efficiency of GA can be judged. This paper proposes the approach to calculating the first expected hitting time and analyzes the bounds of the first hitting time of concrete GA using the proposed approach.

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Cluster Based Medical Image Registration Using Optimized Neural Network

Medical Imaging ◽

10.4018/978-1-5225-0571-6.ch061 ◽

2017 ◽

pp. 1437-1467

Author(s):

Joydev Hazra ◽

Aditi Roy Chowdhury ◽

Paramartha Dutta

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Image Registration ◽

Convergence Rate ◽

Optimization Algorithm ◽

Clustering Algorithms ◽

Data Sets ◽

Learning Method ◽

Local Minima ◽

The Neural Network

Registration of medical images like CT-MR, MR-MR etc. are challenging area for researchers. This chapter introduces a new cluster based registration technique with help of the supervised optimized neural network. Features are extracted from different cluster of an image obtained from clustering algorithms. To overcome the drawback regarding convergence rate of neural network, an optimized neural network is proposed in this chapter. The weights are optimized to increase the convergence rate as well as to avoid stuck in local minima. Different clustering algorithms are explored to minimize the clustering error of an image and extract features from suitable one. The supervised learning method applied to train the neural network. During this training process an optimization algorithm named Genetic Algorithm (GA) is used to update the weights of a neural network. To demonstrate the effectiveness of the proposed method, investigation is carried out on MR T1, T2 data sets. The proposed method shows convincing results in comparison with other existing techniques.

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A Multi-Objective Hybrid Algorithm for Optimization of Grid Structures

International Journal of Applied Mechanics ◽

10.1142/s1758825118500096 ◽

2018 ◽

Vol 10 (01) ◽

pp. 1850009 ◽

Cited By ~ 1

Author(s):

Zhe Xiong ◽

Xiao-Hui Li ◽

Jing-Chang Liang ◽

Li-Juan Li

Keyword(s):

Genetic Algorithm ◽

Particle Swarm Optimization ◽

Convergence Rate ◽

Pareto Optimality ◽

Hybrid Algorithm ◽

Particle Swarm ◽

Mathematical Functions ◽

Swarm Optimization ◽

Multi Objective ◽

Structural Problems

In this study, a novel multi-objective hybrid algorithm (MHGH, multi-objective HPSO-GA hybrid algorithm) is developed by crossing the heuristic particle swarm optimization (HPSO) algorithm with a genetic algorithm (GA) based on the concept of Pareto optimality. To demonstrate the effectiveness of the MHGH, the optimizations of four unconstrained mathematical functions and four constrained truss structural problems are tested and compared to the results using several other classic algorithms. The results show that the MHGH improves the convergence rate and precision of the particle swarm optimization (PSO) and increases its robustness.

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The quasispecies regime for the simple genetic algorithm with roulette wheel selection

Advances in Applied Probability ◽

10.1017/apr.2017.26 ◽

2017 ◽

Vol 49 (3) ◽

pp. 903-926 ◽

Cited By ~ 2

Author(s):

Raphaël Cerf

Keyword(s):

Genetic Algorithm ◽

Selection Mechanism ◽

Roulette Wheel Selection ◽

Mutation Probability ◽

Roulette Wheel ◽

Simple Genetic Algorithm ◽

Crossover Probability ◽

Mean Fitness ◽

The Mean ◽

Best Fit

Abstract We introduce a new parameter to discuss the behavior of a genetic algorithm. This parameter is the mean number of exact copies of the best-fit chromosomes from one generation to the next. We believe that the genetic algorithm operates best when this parameter is slightly larger than 1 and we prove two results supporting this belief. We consider the case of the simple genetic algorithm with the roulette wheel selection mechanism. We denote by ℓ the length of the chromosomes, m the population size, pC the crossover probability, and pM the mutation probability. Our results suggest that the mutation and crossover probabilities should be tuned so that, at each generation, the maximal fitness multiplied by (1 - pC)(1 - pM)ℓ is greater than the mean fitness.

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A new fuzzy adaptive simulated annealing genetic algorithm and its convergence analysis and convergence rate estimation

International Journal of Control Automation and Systems ◽

10.1007/s12555-011-0022-9 ◽

2014 ◽

Vol 12 (3) ◽

pp. 670-679 ◽

Cited By ~ 5

Author(s):

Yonggang Peng ◽

Xiaoping Luo ◽

Wei Wei

Keyword(s):

Genetic Algorithm ◽

Simulated Annealing ◽

Convergence Rate ◽

Convergence Analysis ◽

Rate Estimation ◽

Simulated Annealing Genetic Algorithm ◽

Adaptive Simulated Annealing ◽

Fuzzy Adaptive

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Satellite Module Layout Design Based on Adaptive Bee Evolutionary Genetic Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.538.193 ◽

2014 ◽

Vol 538 ◽

pp. 193-197

Author(s):

Jian Jiang Su ◽

Chao Che ◽

Qiang Zhang ◽

Xiao Peng Wei

Keyword(s):

Genetic Algorithm ◽

Optimal Solution ◽

Layout Design ◽

Adaptive Genetic Algorithm ◽

Local Optimality ◽

Mutation Probability ◽

Evolutionary Genetic ◽

Local Optimal Solution ◽

The Moment ◽

Crossover And Mutation

The main problems for Genetic Algorithm (GA) to deal with the complex layout design of satellite module lie in easily trapping into local optimality and large amount of consuming time. To solve these problems, the Bee Evolutionary Genetic Algorithm (BEGA) and the adaptive genetic algorithm (AGA) are introduced. The crossover operation of BEGA algorithm effectively reinforces the information exploitation of the genetic algorithm, and introducing random individuals in BEGA enhance the exploration capability and avoid the premature convergence of BEGA. These two features enable to accelerate the evolution of the algorithm and maintain excellent solutions. At the same time, AGA is adopted to improve the crossover and mutation probability, which enhances the escaping capability from local optimal solution. Finally, satellite module layout design based on Adaptive Bee Evolutionary Genetic Algorithm (ABEGA) is proposed. Numerical experiments of the satellite module layout optimization show that: ABEGA outperforms SGA and AGA in terms of the overall layout scheme, enveloping circle radius, the moment of inertia and success rate.

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Comparison of Genetic Algorithm and Hill Climbing for Shortest Path Optimization Mapping

E3S Web of Conferences ◽

10.1051/e3sconf/20183111017 ◽

2018 ◽

Vol 31 ◽

pp. 11017

Author(s):

Mona Fronita ◽

Rahmat Gernowo ◽

Vincencius Gunawan

Keyword(s):

Genetic Algorithm ◽

Genetic Algorithms ◽

Shortest Path ◽

Path Optimization ◽

Hill Climbing ◽

Small Cities ◽

Mutation Probability ◽

Optimal Distance ◽

Early Departure ◽

Google Map Api

Traveling Salesman Problem (TSP) is an optimization to find the shortest path to reach several destinations in one trip without passing through the same city and back again to the early departure city, the process is applied to the delivery systems. This comparison is done using two methods, namely optimization genetic algorithm and hill climbing. Hill Climbing works by directly selecting a new path that is exchanged with the neighbour’s to get the track distance smaller than the previous track, without testing. Genetic algorithms depend on the input parameters, they are the number of population, the probability of crossover, mutation probability and the number of generations. To simplify the process of determining the shortest path supported by the development of software that uses the google map API. Tests carried out as much as 20 times with the number of city 8, 16, 24 and 32 to see which method is optimal in terms of distance and time computation. Based on experiments conducted with a number of cities 3, 4, 5 and 6 producing the same value and optimal distance for the genetic algorithm and hill climbing, the value of this distance begins to differ with the number of city 7. The overall results shows that these tests, hill climbing are more optimal to number of small cities and the number of cities over 30 optimized using genetic algorithms.

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Solving Job-Shop Scheduling Problem by an Improved Genetic Algorithm

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.411.588 ◽

2011 ◽

Vol 411 ◽

pp. 588-591

Author(s):

Yan Li Yang ◽

Wei Wei Ke

Keyword(s):

Genetic Algorithm ◽

Convergence Rate ◽

Job Shop ◽

Job Shop Scheduling ◽

Local Optimum ◽

Job Shop Scheduling Problem ◽

Improved Genetic Algorithm ◽

Shop Scheduling ◽

Global Optimal ◽

Simulation Results

An improved genetic algorithm is proposed by introducing selection operation and crossover operation, which overcomes the limitations of the traditional genetic algorithm, avoids the local optimum, improves its convergence rate and the diversity of population, and solves the problems of population prematurity and slow convergence rate in the basic genetic algorithm. Simulation results show that compared with other improved genetic algorithms, the proposed algorithm is better in finding global optimal and convergent rate.

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About the influence of features of fitness-functions on the convergence of the genetic algorithm

PROBLEMS IN PROGRAMMING ◽

10.15407/pp2020.02-03.362 ◽

2020 ◽

pp. 362-367

Author(s):

I.O. Lukianov ◽

◽

F.A. Lytvynenko ◽

Keyword(s):

Genetic Algorithm ◽

Convergence Rate ◽

Random Effect ◽

Optimal Solution ◽

Computer Experiments ◽

Effective Control ◽

Fitness Functions ◽

Parallel Version ◽

Adaptive Capabilities ◽

Significant Factors

The adaptive capabilities of a parallel version of a multipopulation genetic algorithm are considered depending on the characteristics of certain classes of fitness-functions. Ways are proposed to increase the rate of convergence to the optimal solution based on effective control of algorithm parameters and strategies for the exchange of chromosome-solutions between populations. The results of computer experiments with the optimization of fitness-functions with various ratios of insignificant and significant factors are presented. The dependence of the convergence rate of the algorithm in the presence of a random effect on the values of fitness-functions is studied.

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