Revisiting the Simulated Annealing Algorithm from a Teaching Perspective

• Oliveira, Paulo B. de Moura ^[2] ; Pires, Eduardo J. Solteiro ^[2] ; Paulo Novais ^[1]
  1. [1] Universidade do Minho

     Universidade do Minho

     Braga (São José de São Lázaro), Portugal

     
  2. [2] INESC TEC – INESC Technology and Science. Department of Engineering, School of Sciences and Technology (Vila Real, Portugal)
• Localización: International Joint Conference SOCO’16-CISIS’16-ICEUTE’16: San Sebastián, Spain, October 19th-21st, 2016 Proceedings / coord. por Manuel Graña Romay, José Manuel López Guede, Oier Etxaniz, Álvaro Herrero Cosío, Héctor Quintián Pardo, Emilio Santiago Corchado Rodríguez, 2017, ISBN 978-3-319-47364-2, págs. 718-727
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • Hill climbing and simulated annealing are two fundamental search techniques integrating most artificial intelligence and machine learning courses curricula. These techniques serve as introduction to stochastic and probabilistic based metaheuristics. Simulated annealing can be considered a hill-climbing variant with a probabilistic decision. While simulated annealing is conceptually a simple algorithm, in practice it can be difficult to parameterize. In order to promote a good simulated annealing algorithm perception by students, a simulation experiment is reported here. Key implementation issues are addressed, both for minimization and maximization problems. Simulation results are presented.