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 254 articles , 1 standard article )

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  1. Martins, Daniel; Vianna, Gabriel M.; Rosseti, Isabel; Martins, Simone L.; Plastino, Alexandre: Making a state-of-the-art heuristic faster with data mining (2018)
  2. Chen, Yujie; Cowling, Peter; Polack, Fiona; Remde, Stephen; Mourdjis, Philip: Dynamic optimisation of preventative and corrective maintenance schedules for a large scale urban drainage system (2017)
  3. de Souza Lima, Fátima M.; Pereira, Davi S. D.; da Conceição, Samuel V.; de Camargo, Ricardo S.: A multi-objective capacitated rural school bus routing problem with heterogeneous fleet and mixed loads (2017)
  4. González, Miguel A.; Palacios, Juan José; Vela, Camino R.; Hernández-Arauzo, Alejandro: Scatter search for minimizing weighted tardiness in a single machine scheduling with setups (2017)
  5. Kerkhove, L.-P.; Vanhoucke, M.: A parallel multi-objective scatter search for optimising incentive contract design in projects (2017)
  6. Leyman, Pieter; Vanhoucke, Mario: Capital- and resource-constrained project scheduling with net present value optimization (2017)
  7. Marinakis, Yannis; Migdalas, Athanasios; Sifaleras, Angelo: A hybrid particle swarm optimization -- variable neighborhood search algorithm for constrained shortest path problems (2017)
  8. Sánchez-Oro, Jesús; Martínez-Gavara, Anna; Laguna, Manuel; Martí, Rafael; Duarte, Abraham: Variable neighborhood scatter search for the incremental graph drawing problem (2017)
  9. Wang, Yang; Wu, Qinghua; Glover, Fred: Effective metaheuristic algorithms for the minimum differential dispersion problem (2017)
  10. Zhu, Xia; Ruiz, Rubén; Li, Shiyu; Li, Xiaoping: An effective heuristic for project scheduling with resource availability cost (2017)
  11. Amaran, Satyajith; Sahinidis, Nikolaos V.; Sharda, Bikram; Bury, Scott J.: Simulation optimization: a review of algorithms and applications (2016)
  12. Avendaño-Garrido, Martha L.; Gabriel-Argüelles, José R.; Quintana-Torres, Ligia; Mezura-Montes, Efrén: A metaheuristic for a numerical approximation to the mass transfer problem (2016)
  13. Carvalho, Desiree M.; Nascimento, Mariá C. V.: Lagrangian heuristics for the capacitated multi-plant lot sizing problem with multiple periods and items (2016)
  14. Chaves, A. A.; Lorena, L. A. N.; Senne, E. L. F.; Resende, M. G. C.: Hybrid method with CS and BRKGA applied to the minimization of tool switches problem (2016)
  15. Cordeiro, Gauss M.; Lima, Maria do Carmo S.; Gomes, Antonio E.; da-Silva, Cibele Q.; Ortega, Edwin M. M.: The gamma extended Weibull distribution (2016)
  16. Della Croce, Federico; Garraffa, Michele; Salassa, Fabio: A hybrid three-phase approach for the Max-Mean dispersion problem (2016)
  17. El-Shorbagy, M. A.; Mousa, A. A.; Nasr, S. M.: A chaos-based evolutionary algorithm for general nonlinear programming problems (2016)
  18. Ferone, Daniele; Festa, Paola; Resende, Mauricio G. C.: Hybridizations of GRASP with path relinking for the far from most string problem (2016)
  19. Glover, Fred; Hao, Jin-Kao: $f$-flip strategies for unconstrained binary quadratic programming (2016)
  20. He, Jieguang; Chen, Xindu; Chen, Xin: A filter-and-fan approach with adaptive neighborhood switching for resource-constrained project scheduling (2016)

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