Scatter Search

Scatter search This chapter discusses the principles and foundations behind scatter search and its application to the problem of training neural networks. Scatter search is an evolutionary method that has been successfully applied to a wide array of hard optimization problems. Scatter search constructs new trial solutions by combining so-called reference solutions and employing strategic designs that exploit context knowledge. In contrast to other evolutionary methods like genetic algorithms, scatter search is founded on the premise that systematic designs and methods for creating new solutions afford significant benefits beyond those derived from recourse to randomization. Our implementation goal is to create a combination of the five elements in the scatter search methodology that proves effective when searching for optimal weight values in a multilayer neural network. Through experimentation, we show that our instantiation of scatter search can compete with the best-known training algorithms in terms of training quality while keeping the computational effort at a reasonable level. (Source: http://plato.asu.edu)


References in zbMATH (referenced in 286 articles , 1 standard article )

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  1. Ho, Sin C.; Gendreau, Michel: Path relinking for the vehicle routing problem (2006)
  2. Kolisch, Rainer; Hartmann, Sönke: Experimental investigation of heuristics for resource-constrained project scheduling: an update (2006)
  3. Laguna, Manuel; Marti, Rafael: Scatter search (2006)
  4. Lee, Eva K.; Maheshwary, Siddhartha; Mason, Jacquelyn; Glisson, William: Decision support system for mass dispensing of medications for infectious disease outbreaks and bioterrorist attacks (2006)
  5. Martí, Rafael: Scatter search --- wellsprings and challenges (2006)
  6. Martí, Rafael; Laguna, Manuel; Glover, Fred: Principles of scatter search (2006)
  7. Nowicki, Eugeniusz; Smutnicki, Czeslaw: Some aspects of scatter search in the flow-shop problem (2006)
  8. Pacheco, Joaquín; Casado, Silvia; Núñez, Laura; Gómez, Olga: Analysis of new variable selection methods for discriminant analysis (2006)
  9. Pinol, H.; Beasley, J. E.: Scatter search and bionomic algorithms for the aircraft landing problem (2006)
  10. Resende, Mauricio G. C.; Werneck, Renato F.: A hybrid multistart heuristic for the uncapacitated facility location problem (2006)
  11. Russell, Robert A.; Chiang, Wen-Chyuan: Scatter search for the vehicle routing problem with time windows (2006)
  12. Sagarna, Ramón; Lozano, Jose A.: Scatter search in software testing, comparison and collaboration with estimation of distribution algorithms (2006)
  13. Scheuerer, Stephan; Wendolsky, Rolf: A scatter search heuristic for the capacitated clustering problem (2006)
  14. Sergienko, I. V.; Shylo, V. P.: Problems of discrete optimization: challenges and main approaches to solve them (2006)
  15. Sörensen, Kenneth; Sevaux, Marc: (\textMA\mid\textPM): memetic algorithms with population management (2006)
  16. Yagiura, Mutsunori; Ibaraki, Toshihide; Glover, Fred: A path relinking approach with ejection chains for the generalized assignment problem (2006)
  17. Yamashita, Denise Sato; Armentano, Vinícius Amaral; Laguna, Manuel: Scatter search for project scheduling with resource availability cost (2006)
  18. Yavuz, Mesut; Akcali, Elif; Tufekci, Suleyman: A hybrid meta-heuristic for the batching problem in just-in-time flow shops (2006)
  19. Ahmadi, Samad; Osman, Ibrahim H.: Greedy random adaptive memory programming search for the capacitated clustering problem (2005)
  20. Alvarez, Ada M.; González-Velarde, José Luis; De-Alba, Karim: Scatter search for network design problem (2005)

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