Results 21 to 30 of about 3,794 (238)
Hyper-heuristics for personnel scheduling domains
Artificial Intelligence, 2022 In real-life applications problems can frequently change or require small adaptations. Manually creating and tuning algorithms for different problem domains or different versions of a problem can be cumbersome and time-consuming. In this paper we consider several important problems with high practical relevance, which are Bus Driver Scheduling ...
Lucas Kletzander, Nysret Musliu
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Applied Computer Systems, 2022 A problem of minimizing the total weighted tardiness in the preemptive single machine scheduling for discrete manufacturing is considered. A hyper-heuristic is presented, which is composed of 24 various heuristics, to find an approximately optimal ...
Romanuke Vadim
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Sequence-Based Selection Hyper-Heuristic Model via MAP-Elites
IEEE Access, 2021 Although the number of solutions in combinatorial optimization problems (COPs) is finite, some problems grow exponentially and render exact approaches unfeasible. So, approximate methods, such as heuristics, are customary.
Melissa Sanchez +3 more
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Searching the Hyper-heuristic Design Space [PDF]
Cognitive Computation, 2013 We extend a previous mathematical formulation of hyper-heuristics to reflect the emerging generalization of the concept. We show that this leads naturally to a recursive definition of hyper-heuristics and to a division of responsibility that is suggestive of a blackboard architecture, in which individual heuristics annotate a shared workspace with ...
Swan, Jerry +4 more
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Optimizing agents with genetic programming : an evaluation of hyper-heuristics in dynamic real-time logistics [PDF]
, 2017 Dynamic pickup and delivery problems (PDPs) require online algorithms for managing a fleet of vehicles. Generally, vehicles can be managed either centrally or decentrally. A common way to coordinate agents decentrally is to use the contract-net protocol (
Branke, Jürgen +2 more
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Selection hyper-heuristics in dynamic environments [PDF]
Journal of the Operational Research Society, 2013 Current state-of-the-art methodologies are mostly developed for stationary optimization problems. However, many real-world problems are dynamic in nature, where different types of changes may occur over time. Population-based approaches, such as evolutionary algorithms, are frequently used for solving dynamic environment problems.
Kiraz, B., Etaner-Uyar, A. S., Ozcan, E.
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A Novel Cooperative Multi-Stage Hyper-Heuristic for Combination Optimization Problems
Complex System Modeling and Simulation, 2021 A hyper-heuristic algorithm is a general solution framework that adaptively selects the optimizer to address complex problems. A classical hyper-heuristic framework consists of two levels, including the high-level heuristic and a set of low-level ...
Fuqing Zhao +4 more
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A comparison of crossover control mechanisms within single-point selection hyper-heuristics using HyFlex [PDF]
, 2015 Hyper-heuristics are search methodologies which operate at a higher level of abstraction than traditional search and optimisation techniques. Rather than operating on a search space of solutions directly, a hyper-heuristic searches a space of low-level ...
Burke, Edmund K. +2 more
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Khazanah Informatika, 2019 State-of-the-art dari metode yang digunakan untuk menyelesaikan permasalahan optimasi kombinatorik, yang diketahui sebagai permasalahan NP-hard, adalah meta-heuristics.
Arif Djunaidy +2 more
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HyperDE: An Adaptive Hyper-Heuristic for Global Optimization
Algorithms, 2023 In this paper, a novel global optimization approach in the form of an adaptive hyper-heuristic, namely HyperDE, is proposed. As the naming suggests, the method is based on the Differential Evolution (DE) heuristic, which is a well-established ...
Alexandru-Razvan Manescu +1 more
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