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:

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

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  1. Alvarez, Ada M.; González-Velarde, José Luis; De-Alba, Karim: Memetic algorithms (2005)
  2. Aringhieri, Roberto; Dell’Amico, Mauro: Comparing metaheuristic algorithms for SONET network design problems (2005)
  3. Arroyo, José Elias Claudio; Armentano, Vinícius Amaral: Genetic local search for multi-objective flowshop scheduling problems (2005)
  4. Benavent, Enrique; Corberán, Angel; Piñana, Estefanía; Plana, Isaac; Sanchis, José M.: New heuristic algorithms for the windy rural postman problem (2005)
  5. Danna, Emilie; Rothberg, Edward; Le Pape, Claude: Exploring relaxation induced neighborhoods to improve MIP solutions (2005)
  6. Delgado, Cristina; Laguna, Manuel; Pacheco, Joaquín: Minimizing labor requirements in a periodic vehicle loading problem (2005)
  7. Gendreau, Michel; Potvin, Jean-Yves: Metaheuristics in combinatorial optimization (2005)
  8. González, B.; Adenso-Díaz, B.: A bill of materials-based approach for end-of-life decision making in design for the environment (2005)
  9. Gu, Ming; He, Fei; Song, Xiaoyu; Sun, Jiaguang: Multiterminal net assignments by scatter search (2005)
  10. Kochenberger, Gary A.; Glover, Fred; Alidaee, Bahram; Rego, Cesar: An unconstrained quadratic binary programming approach to the vertex coloring problem (2005)
  11. Martí, Rafael; Montes, Francisco; El-Fallahi, Abdellah: An empirical study of learning speed in back-propagation networks (2005)
  12. Melián, Belén; Laguna, Manuel; Moreno-Pérez, José A.: Minimizing the cost of placing and sizing wavelength division multiplexing and optical crossconnect equipment in a telecommunications network (2005)
  13. Oliveira, Carlos A. S.: An algorithm for the maximum likelihood problem on evolutionary trees (2005)
  14. Pacheco, Joaquín A.: A scatter search approach for the minimum sum-of-squares clustering problem (2005)
  15. Pacheco, Joaquín A.; Casado, Silvia: Solving two location models with few facilities by using a hybrid heuristic: a real health resources case (2005)
  16. Scaparra, Maria P.; Church, Richard L.: A GRASP and path relinking heuristic for rural road network development (2005)
  17. Tarantilis, C. D.: Solving the vehicle routing problem with adaptive memory programming methodology (2005)
  18. Voß, Stefan; Fink, Andreas; Duin, Cees: Looking ahead with the pilot method (2005)
  19. Martí, Rafael; El-Fallahi, Abdellah: Multilayer neural networks: an experimental evaluation of on-line training methods (2004)
  20. Glover, Fred; Laguna, Manuel; Marti, Rafael: Scatter search and path relinking: advances and applications (2003)

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