Matches in SemOpenAlex for { <https://semopenalex.org/work/W4386301661> ?p ?o ?g. }
Showing items 1 to 79 of
79
with 100 items per page.
- W4386301661 abstract "Evolutionary algorithms (EA), a class of stochastic search methods based on the principles of natural evolution, have received widespread acclaim for their exceptional performance in various real-world optimization problems. While researchers worldwide have proposed a wide variety of EAs, certain limitations remain, such as slow convergence speed and poor generalization capabilities. Consequently, numerous scholars actively explore improvements to algorithmic structures, operators, search patterns, etc., to enhance their optimization performance. Reinforcement learning (RL) integrated as a component in the EA framework has demonstrated superior performance in recent years. This paper presents a comprehensive survey on integrating reinforcement learning into the evolutionary algorithm, referred to as reinforcement learning-assisted evolutionary algorithm (RL-EA). We begin with the conceptual outlines of reinforcement learning and the evolutionary algorithm. We then provide a taxonomy of RL-EA. Subsequently, we discuss the RL-EA integration method, the RL-assisted strategy adopted by RL-EA, and its applications according to the existing literature. The RL-assisted procedure is divided according to the implemented functions including solution generation, learnable objective function, algorithm/operator/sub-population selection, parameter adaptation, and other strategies. Finally, we analyze potential directions for future research. This survey serves as a rich resource for researchers interested in RL-EA as it overviews the current state-of-the-art and highlights the associated challenges. By leveraging this survey, readers can swiftly gain insights into RL-EA to develop efficient algorithms, thereby fostering further advancements in this emerging field." @default.
- W4386301661 created "2023-08-31" @default.
- W4386301661 creator A5000078546 @default.
- W4386301661 creator A5003799782 @default.
- W4386301661 creator A5006190760 @default.
- W4386301661 creator A5007923079 @default.
- W4386301661 creator A5010776860 @default.
- W4386301661 creator A5011106948 @default.
- W4386301661 creator A5016468933 @default.
- W4386301661 creator A5043565079 @default.
- W4386301661 creator A5055916281 @default.
- W4386301661 creator A5056222697 @default.
- W4386301661 creator A5075051173 @default.
- W4386301661 date "2023-08-25" @default.
- W4386301661 modified "2023-10-16" @default.
- W4386301661 title "Reinforcement Learning-assisted Evolutionary Algorithm: A Survey and Research Opportunities" @default.
- W4386301661 doi "https://doi.org/10.48550/arxiv.2308.13420" @default.
- W4386301661 hasPublicationYear "2023" @default.
- W4386301661 type Work @default.
- W4386301661 citedByCount "0" @default.
- W4386301661 crossrefType "posted-content" @default.
- W4386301661 hasAuthorship W4386301661A5000078546 @default.
- W4386301661 hasAuthorship W4386301661A5003799782 @default.
- W4386301661 hasAuthorship W4386301661A5006190760 @default.
- W4386301661 hasAuthorship W4386301661A5007923079 @default.
- W4386301661 hasAuthorship W4386301661A5010776860 @default.
- W4386301661 hasAuthorship W4386301661A5011106948 @default.
- W4386301661 hasAuthorship W4386301661A5016468933 @default.
- W4386301661 hasAuthorship W4386301661A5043565079 @default.
- W4386301661 hasAuthorship W4386301661A5055916281 @default.
- W4386301661 hasAuthorship W4386301661A5056222697 @default.
- W4386301661 hasAuthorship W4386301661A5075051173 @default.
- W4386301661 hasBestOaLocation W43863016611 @default.
- W4386301661 hasConcept C105902424 @default.
- W4386301661 hasConcept C119857082 @default.
- W4386301661 hasConcept C120665830 @default.
- W4386301661 hasConcept C121332964 @default.
- W4386301661 hasConcept C134306372 @default.
- W4386301661 hasConcept C139807058 @default.
- W4386301661 hasConcept C144024400 @default.
- W4386301661 hasConcept C149923435 @default.
- W4386301661 hasConcept C154945302 @default.
- W4386301661 hasConcept C159149176 @default.
- W4386301661 hasConcept C177148314 @default.
- W4386301661 hasConcept C2908647359 @default.
- W4386301661 hasConcept C33923547 @default.
- W4386301661 hasConcept C41008148 @default.
- W4386301661 hasConcept C97541855 @default.
- W4386301661 hasConceptScore W4386301661C105902424 @default.
- W4386301661 hasConceptScore W4386301661C119857082 @default.
- W4386301661 hasConceptScore W4386301661C120665830 @default.
- W4386301661 hasConceptScore W4386301661C121332964 @default.
- W4386301661 hasConceptScore W4386301661C134306372 @default.
- W4386301661 hasConceptScore W4386301661C139807058 @default.
- W4386301661 hasConceptScore W4386301661C144024400 @default.
- W4386301661 hasConceptScore W4386301661C149923435 @default.
- W4386301661 hasConceptScore W4386301661C154945302 @default.
- W4386301661 hasConceptScore W4386301661C159149176 @default.
- W4386301661 hasConceptScore W4386301661C177148314 @default.
- W4386301661 hasConceptScore W4386301661C2908647359 @default.
- W4386301661 hasConceptScore W4386301661C33923547 @default.
- W4386301661 hasConceptScore W4386301661C41008148 @default.
- W4386301661 hasConceptScore W4386301661C97541855 @default.
- W4386301661 hasLocation W43863016611 @default.
- W4386301661 hasOpenAccess W4386301661 @default.
- W4386301661 hasPrimaryLocation W43863016611 @default.
- W4386301661 hasRelatedWork W1551251808 @default.
- W4386301661 hasRelatedWork W1765898577 @default.
- W4386301661 hasRelatedWork W1951486789 @default.
- W4386301661 hasRelatedWork W2050439249 @default.
- W4386301661 hasRelatedWork W2129785984 @default.
- W4386301661 hasRelatedWork W2218908317 @default.
- W4386301661 hasRelatedWork W2559162946 @default.
- W4386301661 hasRelatedWork W4207066076 @default.
- W4386301661 hasRelatedWork W4319083788 @default.
- W4386301661 hasRelatedWork W4386301661 @default.
- W4386301661 isParatext "false" @default.
- W4386301661 isRetracted "false" @default.
- W4386301661 workType "article" @default.