Abstract
This presentation explores the evolutionary approach called scatter search, which originated from strategies for creating composite decision rules and surrogate constraints. Recent studies have demonstrated the practical advantages of this approach for solving a diverse collection of optimization problems from both classical and real world settings. Scatter search contrasts with other evolutionary procedures, such as genetic algorithms, by providing unifying principles for joining solutions based on generalized path constructions in Euclidean space and by utilizing strategic designs where other approaches resort to randomization. Additional advantages are provided by intensification and diversification mechanisms that exploit adaptive memory, drawing on foundations that link scatter search to tabu search.
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© 2009 Springer-Verlag Berlin Heidelberg
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Laguna, M. (2009). Scatter Search and Path Relinking. In: Ehrgott, M., Fonseca, C.M., Gandibleux, X., Hao, JK., Sevaux, M. (eds) Evolutionary Multi-Criterion Optimization. EMO 2009. Lecture Notes in Computer Science, vol 5467. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01020-0_1
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DOI: https://doi.org/10.1007/978-3-642-01020-0_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01019-4
Online ISBN: 978-3-642-01020-0
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