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Grammar-based generation of variable-selection heuristics for constraint satisfaction problems.

, , , and . Genet. Program. Evolvable Mach., 17 (2): 119-144 (2016)

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Experimental Matching of Instances to Heuristics for Constraint Satisfaction Problems., , , and . Comput. Intell. Neurosci., (2016)Tailoring Instances of the 1D Bin Packing Problem for Assessing Strengths and Weaknesses of Its Solvers., , , , and . PPSN (2), volume 11102 of Lecture Notes in Computer Science, page 373-384. Springer, (2018)A Preliminary Study on Feature-independent Hyper-heuristics for the 0/1 Knapsack Problem., , , , , and . CEC, page 1-8. IEEE, (2020)A Primary Study on Hyper-Heuristics to Customise Metaheuristics for Continuous optimisation., , , , and . CEC, page 1-8. IEEE, (2020)The impact of the bin packing problem structure in hyper-heuristic performance., , and . GECCO (Companion), page 1545-1546. ACM, (2012)Variable and Value Ordering Decision Matrix Hyper-heuristics: A Local Improvement Approach., , , , and . MICAI (1), volume 7094 of Lecture Notes in Computer Science, page 125-136. Springer, (2011)Improving the performance of vector hyper-heuristics through local search., , , , and . GECCO, page 1269-1276. ACM, (2012)Learning vector quantization for variable ordering in constraint satisfaction problems., , and . Pattern Recognit. Lett., 34 (4): 423-432 (2013)Combining Constructive and Perturbative Deep Learning Algorithms for the Capacitated Vehicle Routing Problem., , , , and . CoRR, (2022)Evolving solutions of mixed-model assembly line balancing problems by chaining heuristic optimization methods: track: evolutionary combinatorial optimization and meta-heuristics., , and . GECCO (Companion), page 49-50. ACM, (2011)