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Genetic algorithms: Minimal conditions for convergence [chapter]

Alexandru Agapie
1998 Lecture Notes in Computer Science  
This paper is concerning the finite, homogenous Markov chain modeling of the binary, elitist * genetic algorithm (EGA) and provides a set of minimal sufficient conditions for convergence to the global  ...  Consequently, even one-bit mutations would be sufficient to make the GA globally convergent, because they can be chained to achieve a multi-bit mutation.  ...  Rudolph of Dortmund University for his support and for making the things clear in Markov chain modeling of GAs.  ... 
doi:10.1007/bfb0026600 fatcat:5tvhmruzx5httpob2d4q7xbqo4

Genetic K-means algorithm

K. Krishna, M. Narasimha Murty
1999 IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)  
It is also observed that GKA searches faster than some of the other evolutionary algorithms used for clustering.  ...  To circumvent these expensive operations, we hybridize GA with a classical gradient descent algorithm used in clustering viz., K-means algorithm. Hence, the name genetic K-means algorithm (GKA).  ...  Arvind for his useful comments and suggestions on this paper.  ... 
doi:10.1109/3477.764879 pmid:18252317 fatcat:xr6jkvugqragvniqr3rz5l36by

Multicast Routing Algorithm Based On Genetic Algorithm

Yanhua Chen
2015 International Journal of Future Generation Communication and Networking  
MRAGA (Multicast Routing Algorithm based on Genetic algorithm) for the Delay-Constrained Minimum-Energy Multicast Routing problem was presented.  ...  The genetic operators of this algorithm reduce the transmission delay and energy consumption of multicast trees, thus accelerating the convergence speed of the algorithm.  ...  So the multicast tree adequate for both conditions has higher fitness.  ... 
doi:10.14257/ijfgcn.2015.8.6.08 fatcat:ffwog7grzbfcfd3c63zocvyjgm

Genetic Algorithms [chapter]

Linda L. Hill, Mehmet M. Dalkiliç, Brahim Medjahed, Mourad Ouzzani, Ahmed K. Elmagarmid, Joseph M. Hellerstein, Colin R. Reeves, Christopher B. Jones, Ross S. Purves, Michael F. Goodchild, Jayant Sharma, John Herring (+24 others)
2009 Encyclopedia of Database Systems  
algorithms.  ...  The term genetic algorithm, almost universally abbreviated nowadays to GA, was first used by John Holland [1], whose book Adaptation in Natural and Aritificial Systems of 1975 was instrumental in creating  ...  For the theoretically minded, there is a biennial workshop to consider-the Foundations of Genetic Algorithms [58] [59] [60] [61] [62] [63] .  ... 
doi:10.1007/978-0-387-39940-9_562 fatcat:kez3ycuikfexxeridfim4onnom

Hierarchical Two-Population Genetic Algorithm

Jarno Martikainen, Seppo J. Ovaska
2006 International Journal of Computational Intelligence Research  
Although genetic algorithms are used as a platform for the 2PGA scheme, the principles presented here are applicable also to other population based evolutionary optimization methods.  ...  This paper proposes a new hierarchical two-population genetic algorithm (2PGA).  ...  Acknowledgments The authors whish to thank the referees for their insightful comments. This research work was funded by the Academy of Finland under Grant 214144.  ... 
doi:10.5019/j.ijcir.2006.74 fatcat:rcndrnca2ba3pk6pl45ogczzqy

Genetic algorithms: a survey

M. Srinivas, L.M. Patnaik
1994 Computer  
Genetic algorithm search methods are rooted in the mechanisms of evolution and natural genetics.  ...  Genetic algorithms provide an alternative to traditional optimization techniques by using directed random searches to locate optimal solutions in complex landscapes. n the last five years, genetic algorithms  ...  Characterizing the dynamicsnot a simple taskis important for understanding the conditions under which the G A converges to the global optimum.  ... 
doi:10.1109/2.294849 fatcat:pwvpvkypcfcihh7rss6bfchkuy

Improvements in genetic algorithms

J.A. Vasconcelos, J.A. Ramirez, R.H.C. Takahashi, R.R. Saldanha
2001 IEEE transactions on magnetics  
This paper presents an exhaustive study of the Simple Genetic Algorithm (SGA), Steady State Genetic Algorithm (SSGA) and Replacement Genetic Algorithm (RGA).  ...  For each GA, both sets of best operation types and parameters are found.  ...  Improvements in Genetic Algorithms I. INTRODUCTION T HE USE of a genetic algorithm (GA) requires the choice of a set of genetic operations between many possibilities [1] .  ... 
doi:10.1109/20.952626 fatcat:b7gbnv6tazddnbo4rsxhzdjrxe

Genetic Algorithm and Hybrid Genetic Algorithm for Space Allocation Problems - A Review

Jyoti Sharma, Ravi Shankar Singhal
2014 International Journal of Computer Applications  
Genetic algorithm is an evolutionary approach for solving space layout and optimization problems. Due to some drawbacks in genetic algorithm, several modifications are performed on this algorithm.  ...  Present paper contains a review on genetic algorithm, hybrid genetic algorithm approaches for solving space allocation problems on different sample test like warehouse, shelf, building floors and container  ...  Objects will be placed on the basis of environment conditions and distance from obstacles. (c) Approaches: In this paper, only genetic algorithm and hybrid genetic algorithm are considered.  ... 
doi:10.5120/16585-6283 fatcat:s6uowygq3naqlmb47uzw3gujfi

Hybrid Based Selective Genetic Algorithm

Ahmed Kawther Hussein
2022 International Journal of Engineering Research and Advanced Technology  
In this article, a hybrid based selection is applied under genetic algorithm for solving the problem of WSND.  ...  They have strong power in exploring the solution space and converging toward theoptimal region.  ...  ACKNOWLEDGMENT The author would like to thank the Mustansiriyah university ( Baghdad-Iraq for its support in the present work  ... 
doi:10.31695/ijerat.2022.8.3.1 fatcat:ihv35ys3srekvpv4ciir6szbdu

Theory of genetic algorithms

Lothar M. Schmitt
2001 Theoretical Computer Science  
(iv) The theory includes proof of strong ergodicity for various types of scaled genetic algorithms using common ÿtness selection methods.  ...  (v a) If a certain integrable convergence condition is satisÿed such that the selection pressure increases fast, then there is essentially no other restriction on the crossover operation, and the algorithm  ...  Acknowledgements The author thanks the anonymous referee A for pointing out a number of interesting references in regard to this exposition, and the anonymous referee B for the challenging report.  ... 
doi:10.1016/s0304-3975(00)00406-0 fatcat:lajpildx5zfwvlcuglswn7s6ha

The compact genetic algorithm

G.R. Harik, F.G. Lobo, D.E. Goldberg
1999 IEEE Transactions on Evolutionary Computation  
Index Terms-Bit wise simulated crossover, genetic algorithms, population based incremental learning, probabilistic modeling, univariate marginal distribution algorithm.  ...  This paper introduces the compact genetic algorithm (cGA) which represents the population as a probability distribution over the set of solutions and is operationally equivalent to the order-one behavior  ...  Due to the effects of genetic drift, it takes a long time for both algorithms to fully converge.  ... 
doi:10.1109/4235.797971 fatcat:vqpggedtgrhifbutxabxmyt7wm

Genetic algorithm eclipse mapping [article]

2008 arXiv   pre-print
In this paper we analyse capabilities of eclipse mapping technique, based on genetic algorithm optimization. To model of accretion disk we used the "fire-flies" conception.  ...  The author is thankful to S.V.Kolesnikov for helpful discussions during development of this method.  ...  As a fitting algorithm we have used a Genetic algorithm (Charbonneau 1995) with 100 genes, crossover operations and variable mutation rate.  ... 
arXiv:0801.3059v1 fatcat:357c2pi6lzespibqpq36mg3x3m

Three Proposed Hybrid Genetic Algorithms

Ban Mitras, Nada Hassan
2013 ˜Al-œRafidain journal for computer sciences and mathematics  
Genetic Algorithm has been hybridized with classical optimization methods.  ...  Keywords: genetic algorithm, conjugate gradient method, and steepest descent method. ‫  ...  According to the properties of the genetic algorithm and the conjugate gradient algorithm, the method has the attributes of the global-convergence of the genetic algorithm and the fast convergence of the  ... 
doi:10.33899/csmj.2013.163424 fatcat:64zeiocxpjdybn25bz4voev3u4

Restart scheduling for genetic algorithms [chapter]

Alex S. Fukunaga
1998 Lecture Notes in Computer Science  
In order to escape from local optima, it is standard practice to periodically restart a genetic algorithm according to some restart criteria/policy.  ...  We propose the use of a restart scheduling strategy which generates a static restart strategies with optimal expected utility, based on a database of past performance of the algorithm on a class of problem  ...  Thanks to Andre Stechert for helpful comments on a draft of this paper.  ... 
doi:10.1007/bfb0056878 fatcat:q2hpwrgy3fb2fhltr53waakfhu

Cognitive beamforming using genetic algorithm

N Noori, S M Razavizadeh
2010 2010 IEEE Antennas and Propagation Society International Symposium  
By this definition, the proposed scheme applies genetic algorithm (GA) to find the optimum beamforming weights of the cognitive radio-base station (CR-BS) under signal-to-interference-plus-noise ratio  ...  Consequently, current generation is replaced by the new one to check fitness conditions in the next iteration.  ... 
doi:10.1109/aps.2010.5561810 fatcat:7unuwtk76ncxfgp4czkdsgxmqq
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