@article{Ding_Lü_Li_Shen_Xu_Glover_2019, title={A Two-Individual Based Evolutionary Algorithm for the Flexible Job Shop Scheduling Problem}, volume={33}, url={https://ojs.aaai.org/index.php/AAAI/article/view/4064}, DOI={10.1609/aaai.v33i01.33012262}, abstractNote={<p>Population-based evolutionary algorithms usually manage a large number of individuals to maintain the diversity of the search, which is complex and time-consuming. In this paper, we propose an evolutionary algorithm using only two individuals, called master-apprentice evolutionary algorithm (MAE), for solving the flexible job shop scheduling problem (FJSP). To ensure the diversity and the quality of the evolution, MAE integrates a tabu search procedure, a recombination operator based on path relinking using a novel distance definition, and an effective individual updating strategy, taking into account the multiple complex constraints of FJSP. Experiments on 313 widely-used public instances show that MAE improves the previous best known results for 47 instances and matches the best known results on all except 3 of the remaining instances while consuming the same computational time as current state-of-the-art metaheuristics. MAE additionally establishes solution quality records for 10 hard instances whose previous best values were established by a well-known industrial solver and a state-of-the-art exact method.</p>}, number={01}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Ding, Junwen and Lü, Zhipeng and Li, Chu-Min and Shen, Liji and Xu, Liping and Glover, Fred}, year={2019}, month={Jul.}, pages={2262-2271} }