Introduction
This chapter is devoted to the parametrization of memetic algorithms and how to find a good balance between global and local search. This is one of the most pressing questions when designing a hybrid algorithm. The idea of hybridization is to combine the advantages of different components. But if one components dominates another one, hybridization may become more hindering than useful and computational effort may be wasted. For the case of memetic algorithms, if the effect of local search is too strong, the algorithm may quickly get stuck in local optima of bad quality.Moreover, the algorithm is likely to rediscover the same local optimum over and over again. Lastly, an excessive local search quickly leads to a loss of diversity within the population.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Sudholt, D. (2012). Parametrization and Balancing Local and Global Search. In: Neri, F., Cotta, C., Moscato, P. (eds) Handbook of Memetic Algorithms. Studies in Computational Intelligence, vol 379. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23247-3_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-23247-3_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23246-6
Online ISBN: 978-3-642-23247-3
eBook Packages: EngineeringEngineering (R0)