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Linear analysis of genetic algorithms

Lothar M. Schmitt, Chrystopher L. Nehaniv, Robert H. Fujii
1998 Theoretical Computer Science  
We establish strong ergodicity of the underlying inhomogeneous Markov chain for genetic algorithms that use any of a large class of fitness scaling methods including linear fitness scaling, sigma-truncation  ...  Analysis of this formulation yields new insight into the geometric properties of the three phase mutation, crossover, and fitness selection of a genetic algorithm by representing them as stochastic matrices  ...  The linear model of simple genetic algorithms we discovered was found also independently by Rudolph [25] , who proves a part of our Theorem 15 and analyses a convergent variant of the simple genetic algorithm  ... 
doi:10.1016/s0304-3975(98)00004-8 fatcat:f5jbsqmkmjcbvcqsckow4v7xx4

Analysis of Asymmetric Piecewise Linear Stochastic Resonance Signal Processing Model Based on Genetic Algorithm

Lina He, Chuan Jiang, Zhihan Lv
2020 Complexity  
are optimized by a genetic algorithm.  ...  Finally, the model is applied to bearing fault detection, and an adaptive genetic algorithm is used to optimize the parameters of the system.  ...  In this study, the range of parameter optimization is determined by theoretical analysis, that is, the range of initial gene in genetic algorithm.  ... 
doi:10.1155/2020/8817814 fatcat:e53pgtauhzdg5ar23pkt4gvl5i

Fitting piecewise linear threshold autoregressive models by means of genetic algorithms

R. Baragona, F. Battaglia, D. Cucina
2004 Computational Statistics & Data Analysis  
The proposed approach brings together the genetic algorithm, in its simplest binary form, and some basic features from spline theory.  ...  A nonlinear version of the threshold autoregressive model for time series is introduced.  ...  We proposed an easy method for identifying and estimating the PLTAR model, which is essentially based on a genetic algorithm.  ... 
doi:10.1016/j.csda.2003.11.003 fatcat:yewulfizkjcxfdq2qe32acw7pm

Performance Analysis of Simulated Annealing and Genetic Algorithm on systems of linear equations

Md. Shabiul Islam, Most Tahamina Khatoon, Kazy Noor-e-Alam Siddiquee, Wong Hin Yong, Mohammad Nurul Huda
2021 F1000Research  
Therefore, this study applies the SA to solve the problem of linear equations and evaluates its performances against Genetic Algorithms (GAs), a population-based search meta-heuristic, which are widely  ...  For such large scale global-optimization problem, Simulated Annealing (SA) algorithm and Genetic Algorithm (GA) as meta-heuristics for random search technique perform faster.  ...  The purpose of this research is to study the performances of the SA algorithm while comparing it with Genetic Algorithms (GAs), in solving simultaneous linear equations.  ... 
doi:10.12688/f1000research.73581.1 fatcat:ofr4qdawhfcipmxrvuil7twnie

Linear Discriminant Analysis for An Efficient Diagnosis of Heart Disease via Attribute Filtering Based on Genetic Algorithm

Rania Salah El-Sayed
2018 Journal of Computers  
This system will use attribute filtering techniques genetic algorithm that has been known to be a very adaptive and efficient method of feature selection and reduce number of attributes which indirectly  ...  The classification techniques such as Support Vector Machines, Naive Bayesian Theorem, nearest neighbor and Linear discriminant analysis are used in this paper to know the classification accuracy of the  ...  In our studied using genetic algorithm to optimize classification by LDA enhance the performance of linear discriminant analysis.  ... 
doi:10.17706/jcp.13.11.1290-1299 fatcat:a65qdfvrujhvvkmlmpqke4gxrq

Analysis of the (1+1) EA on Subclasses of Linear Functions under Uniform and Linear Constraints

Tobias Friedrich, Timo Kötzing, Gregor Lagodzinski, Frank Neumann, Martin Schirneck
2017 Proceedings of the 14th ACM/SIGEVO Conference on Foundations of Genetic Algorithms - FOGA '17  
In this paper, we consider the behavior of the classical (1+1) Evolutionary Algorithm for linear functions under linear constraint.  ...  Linear functions have gained a lot of attention in the area of run time analysis of evolutionary computation methods and the corresponding analyses have provided many effective tools for analyzing more  ...  With this paper we have contributed to the area of run time analysis of evolutionary computing by studying classes of linear functions under a given linear constraint.  ... 
doi:10.1145/3040718.3040728 dblp:conf/foga/0001KLNS17 fatcat:wluqxnznlfb7hgq4wv5ewles4i

Non-linear least squares ellipse fitting using the genetic algorithm with applications to strain analysis

Anandaroop Ray, Deepak C. Srivastava
2008 Journal of Structural Geology  
This article outlines both approaches and their relative merits and limitations and, proposes a simple yet powerful non-linear method of solution utilizing the genetic algorithm.  ...  The genetic algorithm method we propose uses geometric as opposed to algebraic fitting.  ...  Richard Lisle provided consistent encouragement and the Department of Science and Technology, India funded this study.  ... 
doi:10.1016/j.jsg.2008.09.003 fatcat:54p5qzxmnnhoplm5q4mauxzw7u

Analysis of Variance of Three Contrast Functions in a Genetic Algorithm for Non-linear Blind Source Separation [chapter]

F. Rojas, J. M. Górriz, O. Valenzuela
2005 Lecture Notes in Computer Science  
In this contribution, we propose and analyze three evaluation functions (contrast functions in Independent Component Analysis terminology) for the use in a genetic algorithm (PNL-GABSS, Post-NonLinear  ...  A thorough analysis of the performance of the chosen contrast functions is made by means of ANOVA (Analysis of Variance), showing the validity of the three approaches.  ...  One of the great advantages of genetic algorithms is its flexibility in the use of evaluation functions.  ... 
doi:10.1007/11494669_128 fatcat:pjupuvzswbbp7hmxikiknclcm4

Comparing genetic algorithms to principal component analysis and linear discriminant analysis in reducing feature dimensionality for speaker recognition

Maider Zamalloa, Luis Javier Rodriguez-Fuentes, Mikel Peñagarikano, Germán Bordel, Juan Pedro Uribe
2008 Proceedings of the 10th annual conference on Genetic and evolutionary computation - GECCO '08  
and Linear Discriminant Analysis (LDA).  ...  In this paper, a feature selection procedure based on genetic algorithms (GA) is presented and compared to two well-known dimensionality reduction techniques, namely Principal Component Analysis (PCA)  ...  ACKNOWLEDGEMENTS This work has been jointly funded by the Government of the Basque Country, under projects S-PE06UN48, S-PE07UN43, S-PE06IK01 and S-PE07IK03, and the University of the Basque Country, under  ... 
doi:10.1145/1389095.1389324 dblp:conf/gecco/ZamalloaRPBU08 fatcat:q5uyzurkqbbpbgfjibg7yukska

Quantitative Structure-Activity Relationship Analysis of the Anticonvulsant Activity of Some Benzylacetamides Based on Genetic Algorithm-Based Multiple Linear Regression

A Najafi, SS Ardakani, M Marjani
2011 Tropical Journal of Pharmaceutical Research  
The relevant molecular descriptors were selected by genetic algorithm-based multiple linear regression (GA-MLR) approach.  ...  Results: The high value of the correlation coefficient, R 2 (0.900), indicate that the model was satisfactory.  ...  Multiple linear regressions (MLR) were used to derive the QSAR equation and feature selection was performed by the use of genetic algorithm (GA).  ... 
doi:10.4314/tjpr.v10i4.14 fatcat:uqshnklax5h65jmaa3mjsju3fi

Study on Condition Monitoring of 2-Spool Turbofan Engine Using Non-Linear Gas Path Analysis Method and Genetic Algorithms

Changduk Kong, Myoungcheol Kang, Gwanglim Park
2013 International Journal of Materials Mechanics and Manufacturing  
Among them the non-linear GPA(Gas Path Analysis) method and the GA(Genetic Algorithms) have lots of advantages to diagnose the engines compared to other advanced condition monitoring methods such as the  ...  Index Terms-Engine condition monitoring, non linear GPA, genetic algorithms, 2-spool turbofan engine.  ...  2-Spool Turbofan Engine Using Non-Linear Gas Path Analysis Method and Genetic Algorithms TABLE II : II TAKE-OFF PERFORMANCE Mach No.  ... 
doi:10.7763/ijmmm.2013.v1.46 fatcat:x5qrbf4gzvdh5c6kgevowcttoq

Non Linear Magnetic Hysteresis Modelling by Finite Volume Method for Jiles-Atherton Model Optimizing by a Genetic Algorithm

Seddik Azzaoui, Kamel Srairi, Mohamed El Hachemi Benbouzid
2011 Journal of Electromagnetic Analysis and Applications  
This paper shows how to extract optimal parameters for the Jiles-Atherton model of hysteresis by a real coded genetic algorithm approach.  ...  The scheme is based upon the definition of modified governing equation derived from Maxwell's equations considered the magnetization M.  ...  Algorithms Introduction Genetic algorithms are developed for the purpose of optimization.  ... 
doi:10.4236/jemaa.2011.36032 fatcat:nb5duc6zmzbslo3ewwzbyej2ge

Study on Condition Monitoring of 2-Spool Turbofan Engine Using Non-Linear GPA(Gas Path Analysis) Method and Genetic Algorithms
2 스풀 터보팬 엔진의 비선형 가스경로 기법과 유전자 알고리즘을 이용한 상태진단 비교연구

Changduk Kong, MyoungCheol Kang, Gwanglim Park
2013 Journal of the Korean Society of Propulsion Engineers  
Among them the non-linear GPA(Gas Path Analysis) method and the GA(Genetic Algorithms) have lots of advantages to diagnose the engines compared to other advanced condition monitoring methods such as the  ...  linear GPA, fuzzy logic and neural networks.  ...  하지만 이러한 GPA 기법들은 성능변수 측정시 의 노이즈나 바이어스 등에 의한 제한을 갖는다. 3.2 유전자 알고리즘 인공지능 기법 중의 하나인 유전자 알고리즘 (Genetic Algorithm; GA)은 최적화 문제와 탐색 문제에 적용이 되고 있으며, 특히 직접계산 기법 보다 직접 검색기법으로 많이 적용되고 있다.  ... 
doi:10.6108/kspe.2013.17.2.071 fatcat:mq4ypv3ncbdabnjsy6p2sla3om

ESTIMATION OF GENETIC AND ENVIRONMENTAL PARAMETERS FOR TWO BINARY TRAITS WITH AN UNDERLYING CONTINUOUS SCALE- A SIMULATION STUDY

2007 Egyptian Journal of Animal Production  
The concerned results from each analysis were heritability and genetic and residual correlations.  ...  Bias and mean squared errors (MSE) of h 2 and genetic and residual correlations estimates were used to assess the quality of h 2 and genetic and residual correlations estimates obtained by different algorithms  ...  Table 4 shows the analysis of variance of genetic and residual correlations.  ... 
doi:10.21608/ejap.2007.93164 fatcat:uf3c6mjlpzc4flxlhicddkte6i

The application research on improvement of genetic algorithm in linear CCD detection

Bo Yu, Zhan-hui Yan, Li-tao Wang, Xiao-dong Li, Da-yu Wang
2017 Vibroengineering PROCEDIA  
Based on the characteristics of linear array CCD detection signal, a genetic algorithm (GA) is established to solve the problem of mathematical model.  ...  ability of the standard genetic algorithm (SGA), premature convergence and low accuracy.  ...  SP based on traditional optimization algorithm and standard genetic algorithm (SGA analysis, points out its insufficiency, and designed a kind of improved genetic algorithm, IGA algorithm is simple, high  ... 
doi:10.21595/vp.2017.18637 fatcat:wzz75gybmnbihnqqwzpg2lmxqi
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