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Effect of Fitness Scaling Functions on Simple Genetic Algorithm

Sarita Kumari
2019 International Journal for Research in Applied Science and Engineering Technology  
Fitness scaling function is used for getting more generalized results in GENETIC ALGORITHM. In this paper we compare many kinds of fitness scaling functions and try to get the suitable one.  ...  Today the most commonly used heuristic method of searching and optimization is the GENETIC ALGORITHM.  ...  The population for a Genetic algorithm ©IJRASET: All Rights are ReservedFig.2 Genetic Algorithm With Scaling Flowchart  ... 
doi:10.22214/ijraset.2019.5265 fatcat:d2er6624wne77l6vagtyhdifju

Comparison of fitness scaling functions in genetic algorithms with applications to optical processing

Farzad Sadjadi, Bahram Javidi, Demetri Psaltis
2004 Optical Information Systems II  
If there are multiple peaks in close proximity, all of nearly the same fitness but with very deep divides, the algorithm will have trouble 'hopping' from one to the other.  ...  Genetic algorithms can optimize these parameters even when the functions they map are fairly complicated, but they can only do so the point where the fitness functions they are given can differentiate  ...  Simple genetic algorithms Simple genetic algorithms search for the most optimum set of variables by using the "survival of the fittest" concept [1] [2] [3] [4] .  ... 
doi:10.1117/12.563910 fatcat:fh3isn3wgnbd7ikcveb3pzfcma

Page 4505 of Mathematical Reviews Vol. , Issue 2000f [page]

2000 Mathematical Reviews  
The linear geometry of genetic operators with applications to the analysis of genetic drift and genetic algorithms using tournament selection.  ...  Linear analysis of genetic algorithms. (English summary) Theoret. Comput. Sci. 200 (1998), no. 1-2, 101-134.  ... 

Linear analysis of genetic algorithms

Lothar M. Schmitt, Chrystopher L. Nehaniv, Robert H. Fujii
1998 Theoretical Computer Science  
We represent simple and fitness-scaled genetic algorithms by Markov chains on probability distributions over the set of all possible populations of a fixed finite size.  ...  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  ...  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

Using Genetic Algorithms in Integer Programming for Decision Support

Youcef Souar
2014 Academic Journal of Interdisciplinary Studies  
A local Mill shapes a suitable case to use Genetic Algorithms in Integer Programming as one of its application forms.  ...  Genetic Algorithms is a new developed quantitative method used in management decision support; it's an Artificial intelligence technique that simulates scientific explanations in genetics and natural evolution  ...  traditional and simple problems with tight search space.  ... 
doi:10.5901/ajis.2014.v3n6p11 fatcat:juk3totidjhu5c5qtifxjadfmi

Research on Genetic Algorithm and Data Information based on Combined Framework for Nonlinear Functions Optimization

Zhigang Ji, Zhenyu Li, Zhiqiang Ji
2011 Procedia Engineering  
Even if section of linearization method widely used the best approximation of the nonlinear function of continuous time a minimum number of piecewise functions did not mention liveried with appropriate  ...  In recent years, piecewise linear change has become an attractive tools, used for all kinds of complicated nonlinear system.  ...  Genetic Algorithm Based Clustering Genetic algorithm is a kind of probability search algorithm iteratively transforms, a set of mathematical objects (usually fixed length binary string), each with a certain  ... 
doi:10.1016/j.proeng.2011.11.2482 fatcat:47tsfb4serf7lmhpzlq5yut6oq

A Multi-Population Genetic Algorithm for Inducing Balanced Decision Trees on Telecommunications Churn Data

V. Podgorelec, S. Karakatic
2013 Elektronika ir Elektrotechnika  
By introducing multiple populations, linear ranking selection and adequate fitness function we were able to avoid overly biased solutions.  ...  As the existing classification methods failed to produce balanced solutions, we developed a new multi-population genetic algorithm for the induction of decision trees.  ...  Genetic Algorithm for Inducing Balanced Decision Trees on Telecommunications Churn Data to adapt to its environment (selection with regard to fitness).  ... 
doi:10.5755/j01.eee.19.6.4578 fatcat:p62ldtpxvndddbfh663ed74rcy

Comparative review of selection techniques in genetic algorithm

Anupriya Shukla, Hari Mohan Pandey, Deepti Mehrotra
2015 2015 International Conference on Futuristic Trends on Computational Analysis and Knowledge Management (ABLAZE)  
This paper compares various selection techniques used in Genetic Algorithm. Genetic algorithms are optimization search algorithms that maximize or minimizes given functions.  ...  Indentifying the appropriate selection technique is a critical step in genetic algorithm.  ...  Steps involved in Simple Genetic Algorithm [16] Figure 1 depicts the working of simple genetic algorithm.  ... 
doi:10.1109/ablaze.2015.7154916 fatcat:cbefsu2ggndcjmuoadsbh3655y

Genetic Algorithms: Basic Ideas, Variants and Analysis [chapter]

R. R.
2007 Vision Systems: Segmentation and Pattern Recognition  
We consider simple genetic algorithm with fixed population size n operates in space of binary string with fixed length m.  ...  As stated above, we consider simple genetic algorithm with fixed population size n operates in space of binary string with fixed length m.  ...  Fuzzy genetic programming combines a simple genetic algorithm that on a context-free language with a context-free fuzzy rule language. 2. Genetic fuzzy systems.  ... 
doi:10.5772/4971 fatcat:f5fgpgf6prai7d5uyu7winwbvu

Comparing genetic operators with gaussian mutations in simulated evolutionary processes using linear systems

D. B. Fogel, J. W. Atmar
1990 Biological cybernetics  
The results indicate that these genetic operators do not compare favorably with more simple random mutation. _____________________________________________________________________________  ...  The efficiency of these operations is evaluated in a series of experiments aimed at solving linear systems of equations.  ...  "Genetic algorithms" are based on specific mechanistic natural genetical systems.  ... 
doi:10.1007/bf00203032 fatcat:csurpqn37zbi3prgwgevafeipa

LINEAR ANTENNA ARRAY DESIGN WITH USE OF GENETIC, MEMETIC AND TABU SEARCH OPTIMIZATION ALGORITHMS

Yavuz Cengiz, Hatice Tokat
2008 Progress In Electromagnetics Research C  
In this work, this paper presents efficient methods of genetic algorithm (GA), memetic algorithm (MA) and tabu search algorithm (TSA) for the synthesis of linear antenna design.  ...  We present three examples of antenna array design to compare the efficiency of the algorithms through simple design to complex design.  ...  Figure 1 . 1 Evolutionary algorithms, (a) Genetic algorithm (b) Tabu search algorithm (c) Memetic algorithm. Figure 2 . 2 Symmetrically placed linear array.  ... 
doi:10.2528/pierc08010205 fatcat:bzpos3ymq5etdeendok55zlrwa

Optimization method for reactive power planning by using a modified simple genetic algorithm

K.Y. Lee, Xiaomin Bai, Young-Moon Park
1995 IEEE Transactions on Power Systems  
Abstpuct- This paper presents an improved simple genetic algorithm developed for reactive power system planning.  ...  However, the simple genetic algorithm has failed in finding the solution except through an extensive number of iterations.  ...  SIMPLE GENETIC ALGORITHM The simple Genetic Algorithm consists of a population of bit strings transformed by three genetic operations: selection or reproduction, crossover and mutation.  ... 
doi:10.1109/59.476049 fatcat:iym37ebzxjg55amvbdavrkqtdm

Function Finding and the Creation of Numerical Constants in Gene Expression Programming [chapter]

Cândida Ferreira
2003 Advances in Soft Computing  
Furthermore, the structural and functional organization of the linear chromosomes allows the unconstrained operation of important genetic operators such as mutation, transposition, and recombination.  ...  The results presented here show that evolutionary algorithms perform considerably worse if numerical constants are explicitly used.  ...  Structurally, genetic algorithms can be subdivided in three fundamental groups: i) Genetic algorithms with individuals consisting of linear chromosomes of fixed length devoid of complex expression.  ... 
doi:10.1007/978-1-4471-3744-3_25 fatcat:rp7cg344tnajlaea2ttiwvy75a

Optimization Using Genetic Algorithms of Aperiodic Linear Phased Arrays for Reduction of Grating Lobes for Wide Scanning

A. Omer, H. Elbanna, A. Mitkees
2011 International Conference on Aerospace Sciences and Aviation Technology  
This method will be optimized using genetic algorithm implemented using MATLAB. The reduction is -9.7 dB for 8-elements and -12.4 dB for 16elements at 60° scan angle.  ...  The need for enlarging the spacing between elements is important either for reduction of mutual coupling or filling specific aperture size with small number of antenna elements.  ...  This error may be due to using a simple genetic algorithm.  ... 
doi:10.21608/asat.2011.23385 fatcat:wusomb6br5es5d44qz34sb5xve

A First Attempt at Constructing Genetic Programming Expressions for EEG Classification [chapter]

César Estébanez, José M. Valls, Ricardo Aler, Inés M. Galván
2005 Lecture Notes in Computer Science  
In the future, we expect that by choosing carefully primitive functions, Genetic Programming will be able to give original results that cannot be matched by other machine learning classification algorithms  ...  In this paper, we use Genetic Programming to evolve projections that translate EEG data into a new vectorial space (coordinates of this space being the new attributes), where projected data can be more  ...  Next, a classification algorithm is applied to the projected data. In this case, we have choosen to apply a Simple linear Perceptron.  ... 
doi:10.1007/11550822_103 fatcat:h7jvfy4xa5hodbiwksqzkn5g6e
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