A new algorithm for reducing metaheuristic design effort

Maria Cristina Riff, Elizabeth Montero

Resultado de la investigación: Contribución a los tipos de informe/libroContribución a la conferencia

25 Citas (Scopus)

Resumen

The process of designing a metaheuristic is a difficult and time consuming task as it usually requires tuning to find the best associated parameter values. In this paper, we propose a simple tuning tool called EVOCA which allows unexperimented metaheuristic designers to obtain good quality results without have a strong knowledge in tuning methods. The simplicity here means that the designer does not have to care about the initial settings of the tuner. We apply EVOCA to a genetic algorithm that solves NK landscape instances of various categories. We show that EVOCA is able to tune both categorical and numerical parameters allowing the designer to discard ineffective components for the algorithm.

Idioma originalInglés
Título de la publicación alojada2013 IEEE Congress on Evolutionary Computation, CEC 2013
Páginas3283-3290
Número de páginas8
DOI
EstadoPublicada - 21 ago 2013
Evento2013 IEEE Congress on Evolutionary Computation, CEC 2013 - Cancun, México
Duración: 20 jun 201323 jun 2013

Conferencia

Conferencia2013 IEEE Congress on Evolutionary Computation, CEC 2013
PaísMéxico
CiudadCancun
Período20/06/1323/06/13

Áreas temáticas de ASJC Scopus

  • Teoría computacional y matemáticas
  • Ciencia computacional teórica

Huella Profundice en los temas de investigación de 'A new algorithm for reducing metaheuristic design effort'. En conjunto forman una huella única.

  • Citar esto

    Riff, M. C., & Montero, E. (2013). A new algorithm for reducing metaheuristic design effort. En 2013 IEEE Congress on Evolutionary Computation, CEC 2013 (pp. 3283-3290). [6557972] https://doi.org/10.1109/CEC.2013.6557972