Filters








33,440 Hits in 5.1 sec

Statistics-Based Adaptive Non-uniform Mutation for Genetic Algorithms [chapter]

Shengxiang Yang
2003 Lecture Notes in Computer Science  
doi:10.1007/3-540-45110-2_53 fatcat:sgt2wqww6ndflmgcqsqecott6q

Evolutionary programming based on non-uniform mutation

Xinchao Zhao, Xiao-Shan Gao, Ze-Chun Hu
2007 Applied Mathematics and Computation  
A new evolutionary programming algorithm (NEP) using the non-uniform mutation operator instead of Gaussian or Cauchy mutation operators is proposed.  ...  According to Michalewicz [1], the parameter b in Eq. (2) of the non-uniform mutation determines the non-uniformity.  ...  For real-coded GAs, the non-uniform mutation operator [1] is introduced.  ... 
doi:10.1016/j.amc.2006.06.107 fatcat:onzr7wvr65fbrisb4kqhbepoze

Crossover and Mutation Operators of Genetic Algorithms

Siew Mooi Lim, Abu Bakar Md. Sultan, Md. Nasir Sulaiman, Aida Mustapha, K. Y. Leong
2017 International Journal of Machine Learning and Computing  
Genetic algorithms (GA) are stimulated by population genetics and evolution at the population level where crossover and mutation comes from random variables.  ...  Section 3 and 4 present the lists of some prevalent mutation and crossover operators.  ...  Multi-non-uniform mutation (MNUM) [33] escalates the genetic diversity of a candidate individual.  ... 
doi:10.18178/ijmlc.2017.7.1.611 fatcat:ctcec4pkjvdzzbzgcxrnbd3cf4

Design of Yacht Course Controller

Xiao Hairong, Pan Weigang
2015 Open Automation and Control Systems Journal  
For this, a non-linear control strategy of active disturbance rejection is suggested, and the adaptive ability of the auto disturbance rejection controller is improved on line by genetic algorithm.  ...  Experiment shows the yacht heading controller has strong robustness and adaptability to environmental interference.  ...  Genetic operation: a simple single point cross way. Variation of adaptive variation. When the adaptation degree is high, the mutation rate is decreased.  ... 
doi:10.2174/1874444301507011352 fatcat:spug6zfmkjaxnalgqtokrgw54m

Genetic algorithm parameters optimization for electrochemical machining using response surface methodology

2017 International Journal of Latest Trends in Engineering and Technology  
In this two Mutation function (Uniform and Adaptive feasible) and three crossover functions (Scattered, Single point and two points) are used for optimization.  ...  Abhishek Tiwari et al (2015) done the optimization of ECM for EN 19 tool steel by using Non-dominated sorting Genetic Algorithm-II (NSGA-II) for maximizing the MRR and minimizing the SR.  ... 
doi:10.21172/1.841.49 fatcat:pwsafot5lzgtnkz57r3gq2rthe

Sets of interacting scalarization functions in local search for multi-objective combinatorial optimization problems

Madalina M. Drugan
2013 2013 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (MCDM)  
The genetic scalarization functions assume that the scalarization functions have commonalities that can be exploited using genetic like operators.  ...  The adaptive scalarization functions select frequently the scalarization function that generates well performing SLS.  ...  A solution s is generated using a uniform random distribution or genetic operators, like for the stochastic PLS.  ... 
doi:10.1109/mcdm.2013.6595442 dblp:conf/cimcdm/Drugan13 fatcat:sk7bz7kfjjguvdxjewtf65r4sa

Individually Directional Evolutionary Algorithm for Solving Global Optimization Problems-Comparative Study

Łukasz Kubuś
2015 International Journal of Intelligent Systems and Applications  
IDEA is a newly developed algorithm for global optimization.  ...  This paper presents the results of simulation analysis of global optimization of benchmark function by Individually Directional Evolutionary Algorithm (IDEA) and other EAs such as Real Coded Genetic Algorithm  ...  Directional non-uniform mutation Directional non-uniform mutation operator is a modified non-uniform mutation operator [8] .  ... 
doi:10.5815/ijisa.2015.09.02 fatcat:ronbc3icyjgrdhski4bjw4dsm4

Self Adaptation of Operator Rates in Evolutionary Algorithms [chapter]

Jonatan Gomez
2004 Lecture Notes in Computer Science  
This work introduces a new evolutionary algorithm that adapts the operator probabilities (rates) while evolves the solution of the problem. Each individual encodes its genetic rates.  ...  We implemented three different genetic operators: Gaussian mutation, Uniform mutation, Single real point crossover.  ...  rate and the crossover rate for a generational and a steady state genetic algorithm.  ... 
doi:10.1007/978-3-540-24854-5_113 fatcat:bena2nt7bvacbc2zw6g6a5xlnq

The most important aspects and operators of genetic algorithm as a stochastic method for solving optimization problems
Najvažniji aspekti i operatori genetskog algoritma kao stohastičke metode za rešavanje optimizacionih problema

Milena Bogdanović
2018 Godisnjak Pedagoskog fakulteta u Vranju  
This paper describes the most important aspects of the genetic algorithm as one of the stochastic methods for solving various classes of optimization problems.  ...  It also describes the basic genetic operators: selection, crossover and mutation, which are serving for a new generation of individuals to achieve optimal or good enough solution of the considered optimization  ...  As for the uniform crossover, it should be noted that for each parental pair defines a binary array of length the same as the genetic parents. This range is called a mask.  ... 
doi:10.5937/gufv1802101b fatcat:onxavt6nkbco5ahnwkv5y3gnly

On Some Basic Concepts of Genetic Algorithms as a Meta-Heuristic Method for Solving of Optimization Problems

Milena Bogdanović
2011 Journal of Software Engineering and Applications  
The paper describes the most important aspects of a genetic algorithm as a stochastic method for solving various classes of optimization problems.  ...  The genetic algorithms represent a family of algorithms using some of genetic principles being present in nature, in order to solve particular computational problems.  ...  For both creep mutation operator required values are random and can have a uniform, exponential, Gaussian or binomial distribution (see [9, 10] ).  ... 
doi:10.4236/jsea.2011.48055 fatcat:7bhvwfj3szaqfc5tncve72uujm

Classification of Human?s Perception of Manipulated and Unmanipulated Digital Images Using Neural Network with Network Reduction Techniques

Zirui Tan, Jo Plested
2019 Australian Journal of Intelligent Information Processing Systems  
algorithm.  ...  In addition to this, we compared the effectiveness of the distinctives network reduction technique when the weights were trained with backpropagation versus genetic algorithm.  ...  Uniform, Uniform, Non-Uniform, Non- Random Adaptive Random Uniform, Adaptive Median test 55.46% 60.66% 60.81% 61.30% 62.48% accuracy Standard 7.26 9.77 8.62 8.49 8.21 deviation Reduction 10% 10% 10% 10%  ... 
dblp:journals/ajiips/TanP19 fatcat:f2qgyz2zlve47di6wrcc5c5pqa

A Self-adaptive Multipeak Artificial Immune Genetic Algorithm

Qingzhao Li, Fei Jiang
2016 TELKOMNIKA (Telecommunication Computing Electronics and Control)  
This paper proposes an self-adaptive multi-peak immune genetic algorithm (SMIGA) and this algorithm integrates immunity thought in the biology immune system into the evolutionary process of genetic algorithm  ...  Genetic algorithm is a global probability search algorithm developed by simulating the biological natural selection and genetic evolution mechanism and it has excellent global search ability, however,  ...  See following formula for specific description of test function: Function f has five peak values with non-uniform distribution and non-uniform altitude.  ... 
doi:10.12928/telkomnika.v14i2.2753 fatcat:xv47qrfeenb4bhrmfdpgp6q57y

An Improved Nonlinear Multi-Objective Optimization Problem Based on Genetic Algorithm

Yali Yun, Yaping Li
2016 International Journal of Hybrid Information Technology  
Genetic algorithms for multi-objective optimization problem to be solved were studied. Through the elitist strategy analysis, it is an improved multi-objective optimization algorithm.  ...  or similar, the algorithm also improved selection operator, so that the algorithm adaptive capacity enhancement, the new algorithm improves the algorithm performance, improves the quality of understanding  ...  In the optimization process, the literature [12] Improved adaptive genetic algorithm crossover and mutation probability design; paper improved genetic algorithm crossover probability and mutation probability  ... 
doi:10.14257/ijhit.2016.9.7.33 fatcat:xm2i7sd35vdtzh33rvcwd7c2fe

An Effective Software Reliability Estimation with Real-Valued Genetic Algorithm

Dr G KrishnaMohan, B Sowmya, K Mohanvamsi, K Sandeep
2018 International Journal of Engineering & Technology  
The operators of GA which is 2 real valued crossovers& mutation of non-uniform interfaced for enhancing SRGM parameters estimation execution and accuracy enhancement.  ...  Instead of these, this can be much adapted for optimizing domain continuously compared to the algorithm of the binary genetic.  ...  heuristic & mutation of non-uniform interfaced for SRGM parameter estimation exactness as well as performance.  ... 
doi:10.14419/ijet.v7i2.32.15713 fatcat:bxqkv2to7ze4dl6tj6i2emf6wa

Encryption and Decryption of a Message Involving Genetic Algorithm

2019 International Journal of Engineering and Advanced Technology  
In the proposed algorithm we use substitution algorithm, genetic crossover and mutation technique.  ...  The aim of this paper is to establish an algorithm for encryption and decryption of a message based on symmetric key cryptosystem involving Genetic Algorithm.  ...  There are various types of mutation techniques such as Gaussian mutation flipping of bits, uniform mutation, non-uniform mutation, boundary mutation and inversion mutation. II.  ... 
doi:10.35940/ijeat.b2379.129219 fatcat:ux7rtcwhjfbz7nq4vyfpxzj5aa
« Previous Showing results 1 — 15 out of 33,440 results