An adaptive hybrid genetic algorithm for pavement management
作者： João Santos , Adelino Ferreira , Gerardo Flintsch
DOI: 10.1080/10298436.2017.1293260
关键词: Selection (genetic algorithm) 、 Engineering 、 Local search (optimization) 、 Control (management) 、 Pavement management 、 Machine learning 、 Process (engineering) 、 Artificial intelligence 、 Mechanism (biology) 、 Set (abstract data type) 、 Genetic algorithm
摘要: AbstractThe pavement maintenance and rehabilitation (M&R) strategy selection problem is an exceedingly hard to solve optimally. In this paper, a novel Adaptive Hybrid Genetic Algorithm (AHGA) proposed which incorporates Local Search (LS) techniques into Algorithms (GA) improve the overall efficiency effectiveness of search. Specifically, it contains two dynamic learning mechanisms guide combine exploration exploitation search processes adaptively. The first mechanism aims assess worthiness conducting LS reactively control computational resources allocated application technique efficiently. second uses instantaneously learned probabilities select from set pre-defined operators compete against each other for most appropriate at any particular stage take over evolutionary-based process. new AHGA compared a...
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sci-hub.se 参考文章(47)
P. Moscato, On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts : Towards Memetic Algorithms Caltech Concurrent Computation Program. ,(1989)
Darrell Whitley, V. Scott Gordon, Keith Mathias, Lamarckian Evolution, The Baldwin Effect and Function Optimization parallel problem solving from nature. pp. 6- 15 ,(1994) , 10.1007/3-540-58484-6_245
Agoston E. Eiben, J. E. Smith, Introduction to evolutionary computing ,(2003)
Adrian A. Hopgood, Tarek A. El-Mihoub, Alan Battersby, Lars Nolle, Hybrid Genetic Algorithms: A Review Engineering Letters. ,vol. 13, pp. 124- 137 ,(2006)
Metaheuristic Procedures for Training Neutral Networks Springer US. ,(2006) , 10.1007/0-387-33416-5
Álvaro Fialho, Luís Da Costa, Marc Schoenauer, Michèle Sebag, Extreme Value Based Adaptive Operator Selection parallel problem solving from nature. pp. 175- 184 ,(2008) , 10.1007/978-3-540-87700-4_18
Felipe P. Espinoza, Barbara S. Minsker, Development of the enhanced self‐adaptive hybrid genetic algorithm (e‐SAHGA) Water Resources Research. ,vol. 42, ,(2006) , 10.1029/2005WR004221
Christopher S. Stokes, Angus R. Simpson, Holger R. Maier, A computational software tool for the minimization of costs and greenhouse gas emissions associated with water distribution systems Environmental Modelling and Software. ,vol. 69, pp. 452- 467 ,(2015) , 10.1016/J.ENVSOFT.2014.11.004
W. Bi, G.C. Dandy, H.R. Maier, Improved genetic algorithm optimization of water distribution system design by incorporating domain knowledge Environmental Modelling and Software. ,vol. 69, pp. 370- 381 ,(2015) , 10.1016/J.ENVSOFT.2014.09.010
Bruno B. Souza, Eduardo G. Carrano, Oriane M. Neto, Ricardo H.C. Takahashi, Immune system memetic algorithm for power distribution network design with load evolution uncertainty Electric Power Systems Research. ,vol. 81, pp. 527- 537 ,(2011) , 10.1016/J.EPSR.2010.10.011