Enzyme Genetic Programming
Proceedings of the 2001 Congress on Evolutionary
Computation, CEC 2001, page 1183--1190. COEX, World Trade Center, 159 Samseong-dong,
Gangnam-gu, Seoul, Korea, IEEE Press, (27--30 May 2001)Abstract
The work reported in this paper follows from the
hypothesis that better performance in artificial
evolution can be achieved by adhering more closely to
the features that make natural evolution effective
within biological systems. An important issue in
evolutionary computation is the choice of solution
representation. Genetic programming, whilst borrowing
from biology in the evolutionary axis of behaviour,
remains firmly rooted in the artificial domain with its
use of a parse tree representation. Following concerns
that this approach does not encourage solution
evolvability, this paper presents an alternative method
modelled upon representations used by biology. Early
results are encouraging; demonstrating that the method
is competitive when applied to problems in the area of
combinatorial circuit design. Whilst too early to gauge
its suitability to a more general domain of
programming, these results do indicate that the concept
of bringing ideas from biological representations to
genetic programming is a promising one.
