Results 21 to 30 of about 7,687 (217)
A Feature-Independent Hyper-Heuristic Approach for Solving the Knapsack Problem
Applied Sciences, 2021 Recent years have witnessed a growing interest in automatic learning mechanisms and applications. The concept of hyper-heuristics, algorithms that either select among existing algorithms or generate new ones, holds high relevance in this matter.
Xavier Sánchez-Díaz +5 more
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Fuzzy adaptive parameter control of a late acceptance hyper-heuristic [PDF]
, 2014 A traditional iterative selection hyper-heuristic which manages a set of low level heuristics relies on two core components, a method for selecting a heuristic to apply at a given point, and a method to decide whether or not to accept the result of the ...
Jackson, Warren G. +2 more
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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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An apprenticeship learning hyper-heuristic for vehicle routing in HyFlex [PDF]
, 2014 Apprenticeship learning occurs via observations while an expert is in action. A hyper-heuristic is a search method or a learning mechanism that controls a set of low level heuristics or combines different heuristic components to generate heuristics for ...
Asta, Shahriar, Özcan, Ender
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The automatic design of hyper-heuristic framework with gene expression programming for combinatorial optimization problems [PDF]
, 2014 Hyper-heuristic approaches aim to automate heuristic design in order to solve multiple problems instead of designing tailor-made methodologies for individual problems.
Ayob, Masri +3 more
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Algorithms, 2021 This paper describes a unique meta-heuristic technique for hybridizing bio-inspired heuristic algorithms. The technique is based on altering the state of agents using a logistic probability function that is dependent on an agent’s fitness rank.
Robertas Damaševičius +1 more
doaj +1 more source
A multi-objective hyper-heuristic based on choice function [PDF]
, 2014 Hyper-heuristics are emerging methodologies that perform a search over the space of heuristics in an attempt to solve difficult computational optimization problems.
Kendall, Graham +2 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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A Classification of Hyper-heuristic Approaches [PDF]
, 2009 The current state of the art in hyper-heuristic research comprises a set of approaches that share the common goal of automating the design and adaptation of heuristic methods to solve hard computational search problems.
A.S. Fukunaga +29 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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