A Software Interface for Supporting the Application of Data Science to Optimisation
by Andrew J. Parkes, Ender Özcan, Daniel Karapetyan
Many real world problems can be solved effectively by metaheuristics in combination with neighbourhood search. However, implementing neighbourhood search for a particular problem domain can be time consuming and so it is important to get the most value from it. Hyper-heuristics aim to get such value by using a specific API such as ‘HyFlex’ to cleanly separate the search control structure from the details of the domain. Here, we discuss various longer-term additions to the HyFlex interface that will allow much richer information exchange, and so enhance learning via data science techniques, but without losing domain independence of the search control.
A Software Interface for Supporting the Application of Data Science to Optimisation (Andrew J. Parkes, Ender Özcan, Daniel Karapetyan), In proc. of Learning and Intelligent Optimization Conference (LION 9), 12–15 January 2015, Lille, France, Lecture Notes in Computer Science 8994, 306–311, Springer, 2015.