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Generalized Hybrid Evolutionary Algorithm Framework with a Mutation Operator Requiring no Adaptation
- Source :
- Lecture Notes in Computer Science ISBN: 9783319687582, SEAL
- Publication Year :
- 2017
- Publisher :
- Springer International Publishing, 2017.
-
Abstract
- This paper presents a generalized hybrid evolutionary optimization structure that not only combines both nondeterministic and deterministic algorithms on their individual merits and distinct advantages, but also offers behaviors of the three originating classes of evolutionary algorithms (EAs). In addition, a robust mutation operator is developed in place of the necessity of mutation adaptation, based on the mutation properties of binary-coded individuals in a genetic algorithm. The behaviour of this mutation operator is examined in full and its performance is compared with adaptive mutations. The results show that the new mutation operator outperforms adaptive mutation operators while reducing complications of extra adaptive parameters in an EA representation.
- Subjects :
- 021103 operations research
Computer science
business.industry
0211 other engineering and technologies
Evolutionary algorithm
02 engineering and technology
Evolutionary computation
Nondeterministic algorithm
Operator (computer programming)
Adaptive mutation
Genetic algorithm
Mutation (genetic algorithm)
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Artificial intelligence
business
Algorithm
Evolutionary programming
Subjects
Details
- ISBN :
- 978-3-319-68758-2
- ISBNs :
- 9783319687582
- Database :
- OpenAIRE
- Journal :
- Lecture Notes in Computer Science ISBN: 9783319687582, SEAL
- Accession number :
- edsair.doi...........3011929abd5bb26516bddca0024e9707