Reinforcement learning versus evolutionary computation: A survey on hybrid algorithms
MM Drugan - Swarm and evolutionary computation, 2019 - Elsevier
A variety of Reinforcement Learning (RL) techniques blends with one or more techniques
from Evolutionary Computation (EC) resulting in hybrid methods classified according to their …
from Evolutionary Computation (EC) resulting in hybrid methods classified according to their …
Rule-based reinforcement learning methodology to inform evolutionary algorithms for constrained optimization of engineering applications
For practical engineering optimization problems, the design space is typically narrow, given
all the real-world constraints. Reinforcement Learning (RL) has commonly been guided by …
all the real-world constraints. Reinforcement Learning (RL) has commonly been guided by …
Recent advances in clonal selection algorithms and applications
Clonal selection algorithms (CSAs) are a kind of Artificial Immune Algorithms (AIAs). In this
paper, recent advances in clonal selection algorithms are summarized and reviewed. First …
paper, recent advances in clonal selection algorithms are summarized and reviewed. First …
A hyper-heuristic ensemble method for static job-shop scheduling
We describe a new hyper-heuristic method NELLI-GP for solving job-shop scheduling
problems (JSSP) that evolves an ensemble of heuristics. The ensemble adopts a divide-and …
problems (JSSP) that evolves an ensemble of heuristics. The ensemble adopts a divide-and …
A clonal selection algorithm for dynamic multimodal function optimization
The objective of dynamic multimodal optimization problems (DMMOPs) is to find all global
optima in a dynamic environment. Although dynamic optimization problems (DOPs) have …
optima in a dynamic environment. Although dynamic optimization problems (DOPs) have …
Instance space analysis and algorithm selection for the job shop scheduling problem
S Strassl, N Musliu - Computers & Operations Research, 2022 - Elsevier
This paper is concerned with the job shop scheduling problem, a well-known, NP-hard
problem that has been extensively studied in the literature, but for which, despite its age and …
problem that has been extensively studied in the literature, but for which, despite its age and …
A tensor based hyper-heuristic for nurse rostering
Nurse rostering is a well-known highly constrained scheduling problem requiring
assignment of shifts to nurses satisfying a variety of constraints. Exact algorithms may fail to …
assignment of shifts to nurses satisfying a variety of constraints. Exact algorithms may fail to …
Hyper-heuristics and Scheduling problems: Strategies, application areas, and performance metrics
Scheduling problems, which involve allocating resources to tasks over specified time
periods to optimize objectives, are crucial in various fields. This work presents hyper …
periods to optimize objectives, are crucial in various fields. This work presents hyper …
Beyond hyper-heuristics: A squared hyper-heuristic model for solving job shop scheduling problems
Hyper-heuristics (HHs) stand as a relatively recent approach to solving optimization
problems. There are different kinds of HHs. One of them deals with how low-level heuristics …
problems. There are different kinds of HHs. One of them deals with how low-level heuristics …
A feature-independent hyper-heuristic approach for solving the knapsack problem
Recent years have witnessed a growing interest in automatic learning mechanisms and
applications. The concept of hyper-heuristics, algorithms that either select among existing …
applications. The concept of hyper-heuristics, algorithms that either select among existing …