Learning behavior in abstract memory schemes for dynamic optimization problems

DOI10.1007/S00500-009-0420-6zbMath1192.68536OpenAlexW2138134560MaRDI QIDQ1034783

Hendrik Richter, Shengxiang Yang

Publication date: 6 November 2009

Published in: Soft Computing (Search for Journal in Brave)

Full work available at URL: http://bura.brunel.ac.uk/handle/2438/5820

zbMATH Keywords

learningevolutionary algorithmdynamic optimization problemmemory dynamics

Mathematics Subject Classification ID

Nonnumerical algorithms (68W05) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (2)

Global memory schemes for dynamic optimization ⋮ Dynamic multi-objective optimization for multi-objective vehicle routing problem with real-time traffic conditions

Cites Work

• Designing evolutionary algorithms for dynamic environments.
• Learnable evolution model: Evolutionary processes guided by machine learning
• AI 2005: Advances in Artificial Intelligence
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