Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/54334
Title: A flexible evolutionary agent: cooperation and competition among real-coded evolutionary operators
Authors: Winter Althaus, Gabriel 
Galván González, Blas José 
Gonzalez, B. 
Jiménez Fránquiz, Juan Ignacio 
Greiner Sánchez, David Juan 
UNESCO Clasification: 3313 Tecnología e ingeniería mecánicas
120304 Inteligencia artificial
1206 Análisis numérico
Keywords: Flexible evolution (FE)
Evolutionary algorithms
Sampling engine
Issue Date: 2005
Journal: Soft Computing 
Abstract: Since it has currently became essential to design more efficient and robust alternative techniques to solve hard optimisation problems in industry or science, and of easy use for practitioners, here a new way of developing simple Artificial Intelligence based Evolutionary Algorithms will be introduced. Our evolutionary computational implementation is a new idea in optimisation. Any evolutionary operators and their associated parameters from well-established evolutionary methods can be considered in such a way that the entire algorithm or intelligent agent-based software performs with very high efficiency without a prior need to investigate which method will be the best for a given optimisation problem.The implementation presented, named Flexible Evolution (FE), has capacity to adapt the operators, the parameters and the algorithm to the circumstances faced at each step of every optimisation run and is able to take into account lessons learned by different research works in the adaptation of operators and parameters. The FE uses Artificial Intelligence concepts to manage internal procedures to adopt decisions and correct the wrong ones. Our aim in this paper will be to give the keys to design these types of procedures, and more specifically, to find the way of achieving an optimum performance of the operators involved in the search, in our case by means of a function included in our algorithm called Sampling Engine. An early implementation has been already developed and tested in our previous works [66–68], so in this paper, new results of a second software implementation are presented comparing the results with those obtained by other methods, using well-known hard test functions.
URI: http://hdl.handle.net/10553/54334
ISSN: 1432-7643
DOI: 10.1007/s00500-004-0381-8
Source: Soft Computing [ISSN 1432-7643], v. 9 (4), p. 299-323
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