Title:	AN ADAPTIVE HYBRID GENETIC ALGORITHM FOR PAVEMENT MANAGEMENT
Author(s):	João Santos, Adelino Ferreira, Gerardo Flintsch
ISBN:	978-989-8533-54-8
Editors:	Piet Kommers and Guo Chao Peng
Year:	2016
Edition:	Single
Keywords:	Genetic algorithms; adaptive local search; hybridization; pavement management; pavement maintenance and rehabilitation costs
Type:	Full Paper
First Page:	211
Last Page:	218
Language:	English
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Paper Abstract:	Throughout the years, Genetic Algorithms (GA) have been successfully applied to tackle the computational complexity of many real-world global optimization problems, such as those faced in determining the optimal long-term maintenance and rehabilitation (M&R) strategies of road pavement sections. However, it is increasingly recognized that pure GA may not be suitable to fine-tune searches in complex combinatorial spaces due to their limited ability to combine, in an optimal way, the exploration of the search space for promising solutions and the exploitation of the best solutions found during the running time of the algorithm. In order to address this drawback, Local Search (LS) techniques have been incorporated into GA to improve the overall efficiency of the search, either by accelerating the discovery of good solutions, for which evolution alone would take too long to find, or by reaching solutions that would otherwise be unreachable by evolution or a local method alone. In this paper, a novel Adaptive Hybrid Genetic Algorithm (AHGA) is proposed which contains two dynamic learning mechanisms to adaptively guide and combine the exploration and exploitation search processes. The first learning mechanism aims to reactively assess the worthiness of conducting an LS, and to efficiently control the computational resources allocated to the application of this search technique. The second learning mechanism uses instantaneously learned probabilities to select, from a set of pre-defined LS operators which compete against each other for selection, which one is the most appropriate for a particular stage of the search to take over from the evolutionary-based search process. The new AHGA is compared to a non-hybridized version of the GA by applying the algorithms to several case studies with the objective of determining the best pavement M&R strategy that minimizes the present value of the total M&R costs. The results show that the proposed AHGA statistically outperforms the traditional GA in terms of efficiency and effectiveness.
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