Hybridisation of Evolutionary Algorithms through Hyper-heuristics for Global Continuous Optimisation
Voß, S. (2016). Hybridisation of Evolutionary Algorithms through Hyper-heuristics for Global Continuous Optimisation. In: (Ed.) Proceedings of the Learning and Intelligent OptimizatioN (LION 10) Conference - Lecture Notes in Computer Science, , () ( ed.). (pp. In Press). : . Springer.
Choosing the correct algorithm to solve a problem still remains an issue 40 years after the Algorithm Selection Problem was first posed. Here we propose a hyper-heuristic which can apply one of two meta-heuristics at the current stage of the search. A scoring function is used to select the most appropriate algorithm based on an estimate of the improvement that might be made by applying each algorithm. We use a differential evolution algorithm and a genetic algorithm as the two meta-heuristics and assess performance on a suite of 18 functions provided by the Generalization-based Contest in Global Optimization (GENOPT). The experimental evaluation shows that the hybridisation is able to provide an improvement with respect to the results obtained by both the differential evolution scheme and the genetic algorithm when they are executed independently. In addition, the high performance of our hybrid approach allowed two out of the three prizes available at genopt to be obtained.
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The Bio-Inspired Algorithms group within the Centre for Algorithms, Visualisation and Evolving Systems is a large and thriving group with interests in nature-inspired computing that include Evolutionary Computing, Hyper-Heuristics, Artificial Immune Systems and Swarm Intelligence.