Implementing hyper-heuristic selection strategies towards creating a synergy between them.
Some existing selection hyper-heuristics by other authors are avaialble: TSHH.java was re-implemented based on the algorithm outlined in the paper (#1) that published it; FS-ILS (#2) was taken from the author's github page (https://github.com/Steven-Adriaensen/FS-ILS)
The folder named Result Data (https://github.com/dubystev/Synergy-HH/tree/master/Result%20Data) stores the raw median objective function values obtained by TS-ILS, an algorithm that improved on the workings of FS-ILS by incorporating some ideas in TSHH and presented in IEEE Congress on Evolutionary Computation 2021 (https://ieeexplore.ieee.org/document/9504841).
The source code for TS-ILS (#3) is named ILS_conf.java; https://github.com/dubystev/Synergy-HH/blob/master/src/hh_project/ILS_conf.java. The result files can be used as basis for comparing the algorithm's performance with other algorithms on the HyFlex test suite (containing benchmark instances for six different combinatorial optimization problems).
The source code for another algorithm from this project, EA-ILS (#4) is publicly available. The selection of perturbative heuristics is achieved via an evolutionary algorithm. Link for EA-ILS: https://github.com/dubystev/Synergy-HH/blob/master/src/hh_project/EA_ILS_final.java
#1. Alanazi, F. (2016). Adaptive Thompson sampling for hyper-heuristics. In 2016 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 1-8). IEEE.
#2. Adriaensen, S., Brys, T., & Nowé, A. (2014). Fair-share ILS: a simple state-of-the-art iterated local search hyperheuristic. In Proceedings of the 2014 annual conference on genetic and evolutionary computation (pp. 1303-1310).
#3. Adubi, S. A., Oladipupo, O. O., & Olugbara, O. O. (2021). Configuring the Perturbation Operations of an Iterated Local Search Algorithm for Cross-domain Search: A Probabilistic Learning Approach. In 2021 IEEE Congress on Evolutionary Computation (CEC) (pp. 1372-1379). IEEE.
#4. Adubi, S. A., Oladipupo, O. O., & Olugbara, O. O. (2022). Evolutionary Algorithm-Based Iterated Local Search Hyper-Heuristic for Combinatorial Optimization Problems. Algorithms, 15(11), 405.