- author = "Ajith Abraham and Nadia Nedjah and Luiza {de Macedo Mourelle}",
- title = "Evolutionary Computation: from Genetic Algorithms to Genetic Programming",
- year = "2006",
- booktitle = "Genetic Systems Programming: Theory and Experiences",
- pages = "1--20",
- volume = "13",
- series = "Studies in Computational Intelligence",
- editor = "Nadia Nedjah and Ajith Abraham and Luiza {de Macedo Mourelle}",
- publisher = "Springer",
- address = "Germany",
- keywords = "genetic algorithms, genetic programming, cartesian genetic programming",
- ISBN = "3-540-29849-5",
-
URL = "
http://www.softcomputing.net/gpsystems.pdf",
-
DOI = "
doi:10.1007/3-540-32498-4_1",
-
abstract = "Evolutionary computation, offers practical advantages
to the researcher facing difficult optimisation
problems. These advantages are multi-fold, including
the simplicity of the approach, its robust response to
changing circumstance, its flexibility, and many other
facets. The evolutionary approach can be applied to
problems where heuristic solutions are not available or
generally lead to unsatisfactory results. As a result,
evolutionary computation have received increased
interest, particularly with regards to the manner in
which they may be applied for practical problem
solving.
we review the development of the field of evolutionary computations from standard genetic algorithms to genetic programming, passing by evolution strategies and evolutionary programming. For each of these orientations, we identify the main differences from the others. We also, describe the most popular variants of genetic programming. These include linear genetic programming (LGP), gene expression programming (GEP), multi-expression programming (MEP), Cartesian genetic programming (CGP), traceless genetic programming (TGP), gramatical evolution (GE) and genetic algorithm for deriving software (GADS).",
- notes = "http://www.springer.com/sgw/cda/frontpage/0,11855,5-146-22-92733168-0,00.html",
- size = "21 pages",
