Аннотация
Much of evolutionary computation was inspired by
Mendelian genetics. But modern genetics has since
advanced considerably, revealing that genes are not
simply parameter settings, but interactive cogs in a
complex chemical machine. At the same time, an
increasing number of evolutionary computation domains
are evolving non-parameterized mechanisms such as
neural networks or symbolic computer programs. As such,
we think modern biological genetics offers much in
helping us understand how to evolve such things. In
this paper, we present a gene regulation model for
Drosophila melanogaster. We then apply gene regulation
to evolve deterministic finite-state automata, and show
that our approach does well compared to past examples
from the literature.
Пользователи данного ресурса
Пожалуйста,
войдите в систему, чтобы принять участие в дискуссии (добавить собственные рецензию, или комментарий)