DATE:  Tuesday, Jan. 30, 2007 
TIME:  2:30 pm 
PLACE:  Council Room (SITE 5084) 
TITLE:  Towards Scalable Genetic Programming 
PRESENTER:  Steffen Christensen Carleton University 
ABSTRACT:
Genetic programming (GP) is a technique for automatically solving
optimization problems where candidate solutions are expressible as
trees with no human intervention. We propose an extension of GP,
termed scalable genetic programming, which solves problems
parameterized by a scalable difficulty parameter. We first define a
taxonomy of evolutionary computation (EC) systems that identifies
variability dimensions and levels for EC systems. We define an
algorithm, the scientist algorithm, which uses genetic programming as
a subroutine to reliably make progress on scalable problems. The
scientist algorithm uses a toolkit of provided routines to progress,
by carrying out experiments to determine the value of different
methods. We define several of the tools for this toolkit. We define
and implement an algorithm for systematically considering all small
trees for a problem. We then use these small trees in an iterative
algorithm to define subroutines that improve performance on a problem
under study. Using this algorithm, we beat the best known
performance on the artificial ant on the Santa Fe trail problem by a
factor of 7.
