@inproceedings { , title = {Hybrid parameter control approach applied to a diversity-based multi-objective Memetic Algorithm for frequency assignment problems}, abstract = {In order to address the difficult issue of parameter setting within a diversity-based Multi-objective Evolutionary Algorithm (MOEA), we recently proposed a hybrid control scheme based on both Fuzzy Logic Controllers (FLCs) and Hyper-heuristics (HHs). The method simultaneously adapts both symbolic and numeric parameters and was shown to be effective when controlling a diversity-based MOEA applied to a range of benchmark problems. Here, we show that the hybrid control scheme generalises to other meta-heuristics by using it to adapt several parameters of a diversity-based multi-objective Memetic Algorithm (MA) applied to a Frequency Assignment Problem (FAP). Using real-world instances of the FAP, we demonstrate that our proposed parameter control method outperforms parameter tuning of the MA. The results provide new evidence that the method can be successfully applied to significantly more complex problems than the benchmarks previously tested.}, conference = {IEEE World Congress on Computational Intelligence}, doi = {10.1109/CEC.2016.7743969}, isbn = {978-1-5090-0623-6}, organization = {Vancouver, Canada}, publicationstatus = {Published}, publisher = {Institute of Electrical and Electronics Engineers}, url = {http://researchrepository.napier.ac.uk/id/eprint/9992}, keyword = {621.381 Electronics, QA75 Electronic computers. Computer science, Optimisation and learning, Centre for Algorithms, Visualisation and Evolving Systems, AI and Technologies, Diversity-based multi-objective evolutionary algorithms, evolutionary algorithms, frequency assignment problems, fuzzy logic controllers, hyper-heuristics;}, year = {2016}, author = {Segredo, Eduardo and Paechter, Ben and Hart, Emma and Gonz´alez-Vila, Carlos Ignacio} }