Improvement of intelligent optimization by an experience feedback approach

  • Authors:
  • Paul Pitiot;Thierry Coudert;Laurent Geneste;Claude Baron

  • Affiliations:
  • Laboratoire Génie de Production, Ecole Nationale d'Ingénieurs de Tarbes, Tarbes, France;Laboratoire Génie de Production, Ecole Nationale d'Ingénieurs de Tarbes, Tarbes, France;Laboratoire Génie de Production, Ecole Nationale d'Ingénieurs de Tarbes, Tarbes, France;Laboratoire d'Etude des Systèmes Informatiques et Automatique, INSA de Toulouse, Toulouse, France

  • Venue:
  • EA'07 Proceedings of the Evolution artificielle, 8th international conference on Artificial evolution
  • Year:
  • 2007

Quantified Score

Hi-index 0.00

Visualization

Abstract

Intelligent optimization is a domain of evolutionary computation thatemerges since a few years. All the methods within this discipline are based onmechanisms for maintaining a set of individuals and, separately, a space ofknowledge linked to the individuals. The aim is to make the individuals evolveto reach better solutions generation after generation using the knowledge linkedto them. The idea proposed in this paper consists in using previous experiencesin order to build the knowledge referential and then accelerate the searchprocess. A method which allows reusing knowledge gained from experiencefeedback is proposed. This approach has been applied to the problem ofselection of project scenario in a multi-objective context. An evolutionaryalgorithm has been modified in order to allow the reuse of capitalizedknowledge. This knowledge is gathered in an influence diagram allowing itsreuse by the algorithm.