Offline-Learning-Based Automated Generation of Metaheuristics

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Design and development of combinatorial optimisation heuristics is a time-consuming and expensive process as it requires expensive and rare expertise. The aim of this research is to create a reusable offline-learning-based metaheuristic generator. We will define a configurable metaheuristic that is capable of modelling a wide range of standard metaheuristics, research algorithm configuration methods specialised for this metaheuristic, provide a convenient interface for the users to apply this framework to their problems and use the framework to objectively compare the performance of various standard metaheuristic approaches. We will apply this framework to well-known combinatorial optimisation problems to evaluate its performance and produce state-of-the-art solution methods.