Self Adaptive Genetic Algorithms

作者: KR Venugopal , KG Srinivasa , LM Patnaik , KR Venugopal , KG Srinivasa

DOI: 10.1007/978-3-642-00193-2_2

关键词: PopulationGenetic representationGenetic algorithmPopulation sizeQuality control and genetic algorithmsData miningClassifier (UML)Computer scienceTestbedCrossover

摘要: … the algorithm in order to detect the best crossovers. A parallel genetic algorithm with dynamic mutation probability … Pi,j[k] is the bit value of the kth bit of individual Pi,j. Let n be the size of …

参考文章(32)
Oscar Montiel, Oscar Castillo, Roberto Sepúlveda, Patricia Melin, None, Application of a breeder genetic algorithm for finite impulse filter optimization Information Sciences. ,vol. 161, pp. 139- 158 ,(2004) , 10.1016/J.INS.2003.05.003
Kenneth Alan De Jong, An analysis of the behavior of a class of genetic adaptive systems. University of Michigan. ,(1975)
F. Herrera, M. Lozano, Gradual distributed real-coded genetic algorithms IEEE Transactions on Evolutionary Computation. ,vol. 4, pp. 43- 63 ,(2000) , 10.1109/4235.843494
Krishna B. Athreya, Hani Doss, Jayaram Sethuraman, ON THE CONVERGENCE OF THE MARKOV CHAIN SIMULATION METHOD Annals of Statistics. ,vol. 24, pp. 69- 100 ,(1996) , 10.1214/AOS/1033066200
Kalyanmoy Deb, Hans-Georg Beyer, Self-Adaptive Genetic Algorithms with Simulated Binary Crossover Evolutionary Computation. ,vol. 9, pp. 197- 221 ,(2001) , 10.1162/106365601750190406
G. Rudolph, Convergence analysis of canonical genetic algorithms IEEE Transactions on Neural Networks. ,vol. 5, pp. 96- 101 ,(1994) , 10.1109/72.265964
A. E. Eiben, E. H. L. Aarts, K. M. Van Hee, Global Convergence of Genetic Algorithms: A Markov Chain Analysis parallel problem solving from nature. pp. 4- 12 ,(1990) , 10.1007/BFB0029725
Walling Cyre, Eric Kee, Sarah Airey, An adaptive genetic algorithm genetic and evolutionary computation conference. pp. 391- 397 ,(2001)
J. H. Holland, Escaping Brittleness: The Possibilities of General-Purpose Learning Algorithms Applied to Parallel Rule-Based Systems Machine Learning: An Artificial Intelligence Approach. ,vol. 2, pp. 593- 623 ,(1986)
Peter Tiňo, John A. Bullinaria, Juan Julián Merelo-Guervós, Xin Yao, Edmund K. Burke, Jonathan E. Rowe, Hans-Paul Schwefel, José A. Lozano, Jim Smith, Ata Kabán, Parallel Problem Solving from Nature - PPSN VIII Springer Berlin Heidelberg. ,(2004) , 10.1007/B100601