Graph lifelong learning: A survey
Graph learning is a popular approach for perfor ming machine learning on graph-structured
data. It has revolutionized the machine learning ability to model graph data to address …
data. It has revolutionized the machine learning ability to model graph data to address …
Designing new metaheuristics: manual versus automatic approaches
A metaheuristic is a collection of algorithmic concepts that can be used to define heuristic
methods applicable to a wide set of optimization problems for which exact/analytical …
methods applicable to a wide set of optimization problems for which exact/analytical …
NeuroCrossover: An intelligent genetic locus selection scheme for genetic algorithm using reinforcement learning
Researchers have been studying genetic algorithms (GAs) extensively in recent decades
and employing them to address extremely challenging combinatorial optimization problems …
and employing them to address extremely challenging combinatorial optimization problems …
Automated design of metaheuristics using reinforcement learning within a novel general search framework
Metaheuristic algorithms have been investigated intensively to address highly complex
combinatorial optimization problems. However, most metaheuristic algorithms have been …
combinatorial optimization problems. However, most metaheuristic algorithms have been …
A selection hyper-heuristic algorithm with Q-learning mechanism
F Zhao, Y Liu, N Zhu, T Xu - Applied Soft Computing, 2023 - Elsevier
The selection of an algorithm in the real world of the application domain is a challenging
problem as no specific algorithm exists capable of solving all issues to a satisfactory …
problem as no specific algorithm exists capable of solving all issues to a satisfactory …
Automated algorithm design using proximal policy optimisation with identified features
Automated algorithm design is attracting considerable recent research attention in solving
complex combinatorial optimisation problems, due to that most metaheuristics may be …
complex combinatorial optimisation problems, due to that most metaheuristics may be …
Automated design of search algorithms based on reinforcement learning
Automated algorithm design has attracted increasing research attention recently in the
evolutionary computation community. The main design decisions include selection …
evolutionary computation community. The main design decisions include selection …
Comparison of schedule generation schemes for designing dispatching rules with genetic programming in the unrelated machines environment
M Đurasević, D Jakobović - Applied Soft Computing, 2020 - Elsevier
Automatically designing new dispatching rules (DRs) by genetic programming has become
an increasingly researched topic. Such an approach enables that DRs can be designed …
an increasingly researched topic. Such an approach enables that DRs can be designed …
Automated design of search algorithms: Learning on algorithmic components
This paper proposes AutoGCOP, a new general framework for automated design of local
search algorithms. In a recently established General Combinatorial Optimisation Problem …
search algorithms. In a recently established General Combinatorial Optimisation Problem …
Automatic hyper-heuristic to generate heuristic-based adaptive sliding mode controller tuners for buck-boost converters
D Zambrano-Gutierrez, J Cruz-Duarte… - Proceedings of the …, 2023 - dl.acm.org
Metaheuristics are commonly used to solve complex and challenging problems, particularly
in electrical system applications. Nevertheless, there is a colorful palette of metaheuristics to …
in electrical system applications. Nevertheless, there is a colorful palette of metaheuristics to …