Successfully engineering Multiobjective Evolutionary Algorithms (MOEAs) involves thoroughly addressing many different issues. However, the performance concepts of efficiency and effectiveness are paramount. MOEAs are stochastic, population-based computational procedures mimicking evolutionary concepts and operations in attempts to find satisfactory, if not optimal, solutions of problems with multiple objectives. Evolutionary Algorithms (EAs) and MOEAs are adaptive stochastic search techniques classified under the umbrella of soft computing; generic EAs such as Genetic Algorithms, Evolution Strategies, Evolutionary Programming, and Genetic Programming are all successfully used in MOEA implementations
Keywords
- Pareto Front
- Test Suite
- Message Passing Interface
- Objective Function Evaluation
- Interprocessor Communication
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, access via your institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
Rights and permissions
Copyright information
© 2007 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
(2007). MOEA Parallelization. In: Evolutionary Algorithms for Solving Multi-Objective Problems. Genetic and Evolutionary Computation Series. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-36797-2_8
Download citation
DOI: https://doi.org/10.1007/978-0-387-36797-2_8
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-33254-3
Online ISBN: 978-0-387-36797-2
eBook Packages: Computer ScienceComputer Science (R0)