Realization of learning induced self-adaptive sampling in noisy optimization
• Proposed three extensions to amend artificial bee colony in noisy fitness landscape. • Stochastic learning automata induced adaptive sample size selection of trial solution. • Local noise distribution captured by local neighborhood fitness variance. • Effective fitness estimate of solution from lo...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Rakshit, Pratyusha [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Umfang: |
28 |
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| Übergeordnetes Werk: |
Enthalten in: Atomic collapse in graphene quantum dots in a magnetic field - Eren, I. ELSEVIER, 2022, the official journal of the World Federation on Soft Computing (WFSC), Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:69 ; year:2018 ; pages:288-315 ; extent:28 |
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| DOI / URN: |
10.1016/j.asoc.2018.04.052 |
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| 520 | |a • Proposed three extensions to amend artificial bee colony in noisy fitness landscape. • Stochastic learning automata induced adaptive sample size selection of trial solution. • Local noise distribution captured by local neighborhood fitness variance. • Effective fitness estimate of solution from local distribution of noisy samples. • Probabilistic selection to avoid deterministic dismissal of quality solutions. | ||
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• Proposed three extensions to amend artificial bee colony in noisy fitness landscape. • Stochastic learning automata induced adaptive sample size selection of trial solution. • Local noise distribution captured by local neighborhood fitness variance. • Effective fitness estimate of solution from local distribution of noisy samples. • Probabilistic selection to avoid deterministic dismissal of quality solutions. |
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• Proposed three extensions to amend artificial bee colony in noisy fitness landscape. • Stochastic learning automata induced adaptive sample size selection of trial solution. • Local noise distribution captured by local neighborhood fitness variance. • Effective fitness estimate of solution from local distribution of noisy samples. • Probabilistic selection to avoid deterministic dismissal of quality solutions. |
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