Genetic Programming Hyper-heuristic with Cluster Awareness for Stochastic Team Orienteering Problem with Time Windows

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Jackson:2020:CEC,

• author = "Jericho Jackson and Yi Mei",
• booktitle = "2020 IEEE Congress on Evolutionary Computation (CEC)",
• title = "Genetic Programming Hyper-heuristic with Cluster Awareness for Stochastic Team Orienteering Problem with Time Windows",
• year = "2020",
• editor = "Yaochu Jin",
• month = "19-24 " # jul,
• keywords = "genetic algorithms, genetic programming, Clustering algorithms, Schedules, Stochastic processes, Real-time systems, Heuristic algorithms, Decision making",
• isbn13 = "978-1-7281-6929-3",
• DOI = "doi:10.1109/CEC48606.2020.9185911",
• abstract = "This paper looks at the stochastic Team orienteering Problem with Time Windows, a well-known problem that models the Personalised Tourist Trip Design Problem. Due to the nature of randomness such as real-time delays, the traditional optimisation approaches are not effective in solving the stochastic problem variant. In this case, genetic programming hyper-heuristics (GPHH) are promising techniques for automatically learning heuristics to make real-time decisions to effectively handle the stochastic environment, however, they still have limitations as the decision making policies use short-sighted information. In this paper, we propose to incorporate global information into the GPHH solution, with a constructed terminal feature based on cluster information to be used by the GPHH, as well as a clustering-aware solution generation process. The experimental studies showed that the newly designed cluster-based feature gave an improvement over the standard GPHH solution. This suggests that incorporating cluster information can be beneficial. Although the clustering-aware solution generation process did not achieve satisfactory performance, the further analysis showed that it could lead to improved performance under certain condition. Overall we demonstrate the effectiveness of using clustering as a global information to enhance the performance of GPHH.",
• notes = "Also known as \cite{9185911}",

}

Genetic Programming entries for Jericho Jackson Yi Mei

Citations