- author = "Lee Altenberg",
- year = "1994",
- pages = "182--187",
- title = "Evolving better representations through selective genome growth",
- booktitle = "Proceedings of the 1st IEEE Conference on Evolutionary Computation",
- publisher = "IEEE",
- address = "Orlando, Florida, USA",
- month = "27-29 " # jun,
- publisher_address = "Piscataway, NJ, USA",
- volume = "1",
- keywords = "genetic algorithms, genetic programming",
-
URL = "
http://dynamics.org/~altenber/PAPERS/EBR/",
-
URL = "
http://dynamics.org/Altenberg/FILES/LeeEBR.pdf",
-
abstract = "The choice of how to represent the search space for a
genetic algorithm (GA) is critical to the GA's
performance. Representations are usually engineered by
hand and fixed for the duration of the GA run. Here a
new method is described in which the degrees of freedom
of the representation --- i.e. the genes -- are
increased incrementally. The phenotypic effects of the
new genes are randomly drawn from a space of different
functional effects. Only those genes that initially
increase fitness are kept. The genotype-phenotype map
that results from this selection during the
constructional of the genome allows better adaptation.
This effect is illustrated with the NK landscape model.
The resulting genotype-phenotype maps are much less
epistatic than generic maps would be. They have
extremely low values of ``K'' --- the number of fitness
components affected by each gene. Moreover, these maps
are exquisitely tuned to the specifics of the random
fitness functions, and achieve fitnesses many standard
deviations above generic NK landscapes with the same
\gp\ maps. The evolved maps create adaptive landscapes
that are much smoother than generic NK landscapes ever
are. Thus a caveat should be made when making arguments
about the applicability of generic properties of
complex systems to evolved systems. This method may
help to solve the problem of choice of representations
in genetic algorithms.
Copyright 1996 Lee Altenberg",
-
notes = "
",
