- author = "J-L Segapeli and C. Escazut and P. Collard",
- title = "DGP: How To Improve Genetic Programming with Duals",
- booktitle = "Artificial Neural Nets and Genetic Algorithms: Proceedings of the International Conference, ICANNGA97",
- year = "1997",
- editor = "George D. Smith and Nigel C. Steele and Rudolf F. Albrecht",
- pages = "409--413",
- address = "University of East Anglia, Norwich, UK",
- publisher = "Springer-Verlag",
- note = "published in 1998",
- keywords = "genetic algorithms, genetic programming",
- ISBN = "3-211-83087-1",
-
DOI = "
doi:10.1007/978-3-7091-6492-1_90",
- abstract = "In this paper, we present a new approach, improving the performances of a genetic algorithm (GA). Such algorithms are iterative search procedures based on natural genetics. We use an original genetic algorithm that manipulates pairs of twins in its population: DGA, dual-based genetic algorithm. We show that this approach is relevant for genetic programming (GP), which manipulates populations of trees. In particular, we show that duals can transform a deceptive problem into a convergent one. We also prove that using pairs of dual functions in the primitive function set, is more efficient in the problem of learning boolean functions. Here, in order to prove the theoretical interest of our approach (DGP: dual-based genetic programming), we perform a numerical simulation.",
-
notes = "http://www.sys.uea.ac.uk/Research/ResGroups/MAG/ICANNGA97/papers_frame.html
http://www.springer.com/sgw/cda/frontpage/0,11855,1-102-22-1595876-0,00.html?changeHeader=true",
