Efrén Mezura-Montes
Artificial Intelligence Research Center, University of Veracruz, MEXICO.
emezura@uv.mx
Helio J.C.
Barbosa
National Laboratory for Scientific Computation (LNCC), BRAZIL.
Universidade Federal de Juiz de Fora, BRAZIL.
Rituparna
Datta
Computer Science, University of South Alabama
USA
In their original versions, nature-inspired algorithms for optimization such as evolutionary algorithms (EAs) and swarm intelligence algorithms (SIAs) are designed to sample unconstrained search spaces. Therefore, a considerable amount of research has been dedicated to adapt them to deal with constrained search spaces. The objective of the session is to present the most recent advances in constrained optimization using different nature-inspired algorithms.
Scope
The session seeks to promote the discussion and presentation of novel works related with (but not limited to) the following issues:
- Novel constraint-handling techniques for EAs and SIAs
- Novel constraint-handling techniques for constrained dynamic optimization
- Novel/adapted search algorithms for constrained optimization
- Memetic algorithms in constrained search spaces
- Parameter setting (tuning and control) in constrained optimization
- Mixed (discrete-continuous) constrained optimization
- Theoretical analysis and complexity of algorithms in constrained optimization
- Convergence analysis in constrained optimization
- Performance evaluation of algorithms in constrained optimization
- Expensive Constrained Optimization
- Design of difficult and scalable test functions
- Applications
Program Committee
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To be announced.
Please follow the CEC 2019 instructions for authors.
Copyright 2018 Mauricio Damián Araoz. All rights reserved.