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An Exploratory Study on Machine-Learning-Based Hyper-heuristics for the Knapsack Problem
Hyper-heuristics have risen as a recurrent method to solve combinatorial optimization problems since they use a set of heuristics selectively...
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Online Learning Hyper-Heuristics in Multi-Objective Evolutionary Algorithms
Well-defined Hyper-Heuristics enhance the generalization of MOEAs and the blind usability on complex and even dynamic real-world application....
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Test Case Features as Hyper-heuristics for Inductive Programming
Instruction subsets are heuristics that can reduce the size of the inductive programming search space by tens of orders of magnitude. Comprising many...
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Automated Design of Dynamic Heuristic Set Selection for Cross-Domain Selection Hyper-Heuristics
Selection hyper-heuristics have been used successfully to solve hard optimization problems. These techniques choose a heuristic or a group of...
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Rigorous Performance Analysis of Hyper-heuristics
We provide an overview of the state-of-the-art in the time complexity analysis of selection hyper-heuristics for combinatorial optimisation. These...
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Hyper-heuristics: Autonomous Problem Solvers
Algorithm design is a general task for any problem-solving scenario. For Search and Optimization, this task becomes rather challenging due to the...
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Dynamic Heuristic Set Selection for Cross-Domain Selection Hyper-heuristics
Selection hyper-heuristics have proven to be effective in solving various real-world problems. Hyper-heuristics differ from traditional heuristic...
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Ant-Based Hyper-Heuristics for the Movie Scene Scheduling Problem
The paper provides a study of the use of hyper-heuristics on the movie scene scheduling problem. In particular, the paper extends the definition of...
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Dynamic Learning in Hyper-Heuristics to Solve Flowshop Problems
Hyper-heuristics (HHs) are algorithms suitable for designing heuristics. HHs perform the search divided in two levels: they look for heuristic...
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Knowledge Transfer in Genetic Programming Hyper-heuristics
Genetic Programming Hyper-heuristics (GPHHs) have been successfully applied in various problem domains for automatically designing heuristics such as...
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Ant-Based Generation Constructive Hyper-heuristics for the Movie Scene Scheduling Problem
The task of generation constructive hyper-heuristics concerns itself with generating new heuristics for problem domains via some kind of mechanism...
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Stochastic online decisioning hyper-heuristic for high dimensional optimization
Most existing heuristic optimizers are found to be restricted to problems of moderate dimensionality, and their performance suffers when solving...
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A novel evolutionary status guided hyper-heuristic algorithm for continuous optimization
This paper proposes a novel evolutionary status guided hyper-heuristic algorithm named ES-HHA for continuous optimization. A representative...
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Evolutionary multi-mode slime mold optimization: a hyper-heuristic algorithm inspired by slime mold foraging behaviors
This paper proposes a novel hyper-heuristic algorithm termed evolutionary multi-mode slime mold optimization (EMSMO) for addressing continuous...
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Evolving ensembles of heuristics for the travelling salesman problem
The Travelling Salesman Problem (TSP) is a well-known optimisation problem that has been widely studied over the last century. As a result, a variety...
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Elitism-Based Genetic Algorithm Hyper-heuristic for Solving Real-Life Surgical Scheduling Problem
Hyper-heuristic was designed to automate the development of computational search methodologies. Although it has effectively handled a variety of...
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A Preliminary Study on Score-Based Hyper-heuristics for Solving the Bin Packing Problem
The bin packing problem is a widespread combinatorial problem. It aims at packing a set of items by using as few bins as possible. Among the many...
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Parameter Control and Policy Control
This chapter aims to explain some basic methods for parameter control and strategy control, which have a significant impact on the performance of...
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EHHR: an efficient evolutionary hyper-heuristic based recommender framework for short-text classifier selection
With various machine learning heuristics, it becomes difficult to choose an appropriate heuristic to classify short-text emerging from various social...
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Combining hyper-heuristics to evolve ensembles of priority rules for on-line scheduling
Combining metaheuristics is a common technique that may produce high quality solutions to complex problems. In this paper, we propose a combination...
