IEEE ESCO Taskforce
IEEE ESCO Taskforce
  • Видео 8
  • Просмотров 702
Meta-Black-Box Optimization
IEEE ESCO Webinar #16: Meta-Black-Box Optimization: From Automatic Algorithm Configuration to Automatic Algorithm Generation, by Prof Yue-Jiao Gong from South China University of Technology.
Abstract
Meta-Black-Box Optimization (MetaBBO) leverages a meta-level learner to automate the black-box optimization process, thereby enhancing efficiency and reducing human intervention. MetaBBO can be categorized into three distinct branches: the first focuses on automatic algorithm configuration or selection; the second employs neural networks to propose candidate solutions directly; and the third generates algorithms, as demonstrated in our study, by producing update rules expressed as closed-form e...
Просмотров: 16

Видео

IEEE ESCO Taskforce Webinar #20: Evolutionary Machine Learning in Business OptimisationIEEE ESCO Taskforce Webinar #20: Evolutionary Machine Learning in Business Optimisation
IEEE ESCO Taskforce Webinar #20: Evolutionary Machine Learning in Business Optimisation
Просмотров 30Месяц назад
Speaker: Su Nguyen, RMIT University, Australia Abstract: The complexity of optimisation models has increased significantly because of new sophisticated business models and the ever-changing digital landscape. Solving optimisation models quickly and accurately is challenging. To tackle these challenges, recent research has explored the applications of machine learning to design or enhance optimi...
Automated Design of Selection Hyper-heuristicsAutomated Design of Selection Hyper-heuristics
Automated Design of Selection Hyper-heuristics
Просмотров 252 месяца назад
Webinar #19 organised by the IEEE Taskforce on Evolutionary Scheduling and Combinatorial Optimisation. The talk is given by Prof Ender Ozcan, from University of Nottingham, UK. Abstract: Hyper-heuristics encompass a range of approaches that facilitate the automated design of computational search methods. The current state-of-the-art in hyper-heuristic research includes various classes of algori...
Neural Combinatorial Optimization with Heavy Decoder: Toward Large Scale GeneralizationNeural Combinatorial Optimization with Heavy Decoder: Toward Large Scale Generalization
Neural Combinatorial Optimization with Heavy Decoder: Toward Large Scale Generalization
Просмотров 732 месяца назад
IEEE Taskforce on Evolutionary Scheduling and Combinatorial Optimisation Webinar #18, by Dr. Zhenkun Wang from Southern University of Science and Technology. More information can be found at homepages.ecs.vuw.ac.nz/~yimei/ieee-tf-esco/ecso-webinars.html Neural Combinatorial Optimization (NCO) aims to learn directly from data a neural network that can solve complex combinatorial optimization pro...
Collaborative Deep Reinforcement Learning for Solving Multi-Objective Vehicle Routing ProblemsCollaborative Deep Reinforcement Learning for Solving Multi-Objective Vehicle Routing Problems
Collaborative Deep Reinforcement Learning for Solving Multi-Objective Vehicle Routing Problems
Просмотров 1226 месяцев назад
Existing deep reinforcement learning (DRL) methods for multi-objective vehicle routing problems (MOVRPs) typically decompose an MOVRP into subproblems with respective preferences and then train policies to solve corresponding subproblems. However, such a paradigm is still less effective in tackling the intricate interactions among subproblems, thus holding back the quality of the Pareto solutio...
Cross-domain Algorithm Selection: Algorithm Selection across Selection Hyper-heuristicsCross-domain Algorithm Selection: Algorithm Selection across Selection Hyper-heuristics
Cross-domain Algorithm Selection: Algorithm Selection across Selection Hyper-heuristics
Просмотров 126Год назад
Hyper-heuristics are problem-independent methods that are used to solve various instances of different problem domains. There have been effective hyper-heuristic designs in the literature that provide a certain level of generality in problem solving. Nonetheless, existing research indicates that no single hyper-heuristic performs consistently best in different problem-solving scenarios. Algorit...
Combinatorial Optimisation Can be Different from Continuous Optimisation for MOEAsCombinatorial Optimisation Can be Different from Continuous Optimisation for MOEAs
Combinatorial Optimisation Can be Different from Continuous Optimisation for MOEAs
Просмотров 73Год назад
IEEE Taskforce on Evolutionary Scheduling and Combinatorial Optimisation Webinar Series #10: Combinatorial Optimisation Can be Different from Continuous Optimisation for MOEAs Speaker: Miqing Li, Assistant Professor, University of Birmingham, UK
Solution Prediction via Machine Learning for Combinatorial OptimizationSolution Prediction via Machine Learning for Combinatorial Optimization
Solution Prediction via Machine Learning for Combinatorial Optimization
Просмотров 2392 года назад
The Webinar given by Prof Xiaodong Li from RMIT University, Australia. Organiser: IEEE Taskforce on Evolutionary Scheduling and Combinatorial Optimisation (homepages.ecs.vuw.ac.nz/~yimei/ieee-tf-esco/) Abstract Combinatorial optimization problems are ubiquitous across many disciplinary areas such as science and engineering. In the big data era, the dimensionality of a combinatorial optimization...