Created by W.Langdon from gp-bibliography.bib Revision:1.2031
@InProceedings{icec94:maxwell,
author = "Sidney R. {Maxwell III}",
title = "Experiments with a Coroutine Model for Genetic
Programming",
booktitle = "Proceedings of the 1994 IEEE World Congress on
Computational Intelligence",
year = "1994",
pages = "413--417a",
volume = "1",
address = "Orlando, Florida, USA",
month = "27-29 " # jun,
organisation = "IEEE",
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming, parallel
programming, subroutines, iterative methods, coroutine
execution model, synchronous parallel program
execution, fitness comparison, execution time limits,
iteration limits, infinite loops, infinite recursion,
evolutionary progress, population tolerance",
ISBN = "0-7803-1899-4",
size = "6 pages",
URL = "
http://ieeexplore.ieee.org/iel2/1125/8059/00349915.pdf?isNumber=8059",
doi = "
doi:10.1109/ICEC.1994.349915",
abstract = "The genetic programming methodology is expanded with a
coroutine model for the synchronous, parallel execution
of the individual programs in the population. For
certain classes of problem, namely those that support
fitness comparison between individuals which are in a
state of execution, this model allows the removal of
execution time and iteration limits. Populations can
then tolerate individuals with infinite loops (or in a
suitable environment, infinite recursion), while still
allowing evolutionary progress.",
notes = "Earlier version 11 pages available electronically. See
genetic-programming mailing list 14/12/93, 4/1/94 and
5/1/94
coroutine _model_ is described in terms of real program
runtimes. Actually achieved by defining psuedo elapse
time for each instruction (which is zero in some cases)
and interrupting execution of the program after a
certain number of these timesteps. Makes things
controlable.
Run on Artificial Ant Santa Fe Trail and claims better
programs produced with less effort than Koza
(GP1).
Steady state pop of 1000, with 100 new individuals per
cycle. Limit of 600 ticks (when comparing with
\cite{koza:book}) Faster programs preferred. {"}The
coroutine model found individuals which were more
efficient (faster?) in solving the problem than the
generational model{"} p417
Date: Mon, 24 Apr 2000 09:36:34 -0700 From: {"}Sidney R
Maxwell III{"} > 1-How did Maxwell implement his
method?
Basically, I executed each individual a fixed number of
steps (a 'configurable' number N, with a value of as
little as1). Individuals added to the population were
pre-executed an appropraite number of steps to ensure
that all individuals in the population had executed the
same number of steps.
The problem that I was tackling was the Artificial Ant,
for which evaluating fitness on partially executed
individuals was meaningful.
In early experiments, I executed all individuals in the
population N steps. Later, as a run-time performance
enhancement, I [simply] ensured that individuals being
evaluated had executed the same number of steps before
comparing their fitness.
Cf. Levin search.",
}
Genetic Programming entries for Sidney R Maxwell III