Study of Evolutionary Algorithms for Multi-objective Optimization

There are two prominent principles of any information retrieval system precision and analysis. Precision is the proportion of correct documents retrieved by the information retrieval system to the total number of archives retrieved in the perspective of a client's inquiry. Analysis includes the...

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Veröffentlicht in:SN computer science Jg. 3; H. 5; S. 409
Hauptverfasser: Gaikwad, Rama, Lakshmanan, Ramanathan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Singapore Springer Nature Singapore 01.09.2022
Springer Nature B.V
Schlagworte:
Big Data
Computer Imaging
Computer Science
Computer Systems Organization and Communication Networks
Data Structures and Information Theory
Decision theory
Documents
Efficiency
Energy consumption
Evolutionary algorithms
Genetic algorithms
Image retrieval
Information retrieval
Information Systems and Communication Service
Keywords
Language
Multiple objective analysis
Neural networks
Objectives
Optimization
Pattern Recognition and Graphics
Predictive Artificial Intelligence for Cyber Security and Privacy
Relevance feedback
Review Article
Semantics
Software Engineering/Programming and Operating Systems
Vision
Information retrieval systems
Multi-objective evolutionary algorithms
Query learning
ISSN:2661-8907, 2662-995X, 2661-8907
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Zusammenfassung:There are two prominent principles of any information retrieval system precision and analysis. Precision is the proportion of correct documents retrieved by the information retrieval system to the total number of archives retrieved in the perspective of a client's inquiry. Analysis includes the way these documents are expected to be retrieved by the client. Predicting the documents that the user might need in the upcoming time is also crucial. Having the list of multiple objectives in the user query and getting the optimum result adhering to the objectives are important these days where we can make use of evolutionary algorithms which provides set of solutions to the problem. We present here in this paper study of useful multi-objective optimization algorithms, recent developments in multi-objective evolutionary algorithms, and literature which have used these algorithms. Further, we discussed our proposed system where we will make use of the knowledge gained from this examination.
Bibliographie:ObjectType-Article-1
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ISSN:2661-8907
2662-995X
2661-8907
DOI:10.1007/s42979-022-01283-x