Multi-objective meta level soft computing-based evolutionary structural design

DOI10.1016/J.JFRANKLIN.2006.03.016zbMath1269.90098OpenAlexW2084896770MaRDI QIDQ357983

Amir-R. Khorsand, Mohammad-R. Akbarzadeh-T

Publication date: 15 August 2013

Published in: Journal of the Franklin Institute (Search for Journal in Brave)

Full work available at URL: http://www.sciencedirect.com/science/article/pii/S0016003206000731

zbMATH Keywords

fuzzy logicfinite element analysis (FEA)genetic algorithms (GA)meta GAmulti-objective GAneural networks (NN)structural design (SD)

Mathematics Subject Classification ID

Multi-objective and goal programming (90C29) Approximation methods and heuristics in mathematical programming (90C59) Queues and service in operations research (90B22)

Related Items (1)

Variable-structure-systems based approach for online learning of spiking neural networks and its experimental evaluation

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• How to select and how to rank projects: The PROMETHEE method
• An evaluation of back-propagation neural networks for the optimal design of structural systems. I: Training procedures
• An evaluation of back-propagation neural networks for the optimal design of structural systems. II: Numerical evaluation
• Reliability-based structural optimization using neural networks and Monte Carlo simulation
• Structural optimization using evolution strategies and neural networks
• Approximation by superpositions of a sigmoidal function

This page was built for publication: Multi-objective meta level soft computing-based evolutionary structural design