An Effective Quantum Inspired Genetic Algorithm for Continuous Multiobjective Optimization

Multiobjective Optimization Problems (MOP) can be found in many issues of scientific research, engineering, and in everyday social life. A MOP problem has several objectives that conflict with one another which must be optimized simultaneously. This paper presents a quantum-inspired evolutionary alg...

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Veröffentlicht in:	2019 5th International Conference on Science in Information Technology (ICSITech) S. 161 - 166
Hauptverfasser:	Abraham, Julisa Bana, Wahyunggoro, Oyas, Setiawan, Noor Akhmad
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.10.2019
Schlagworte:	Evolutionary computation Fast Nondominated Sorting Genetic Algorithm Genetic algorithms Information technology Logic gates Measurement Optimization Quantum Inspired Evolutionary Algorithm Sorting Testing
ISBN:	9781728123783, 172812378X
Online-Zugang:	Volltext
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