abstract = "We present a genetic programming (GP) system to evolve
reusable heuristics for the 2-D strip packing problem.
The evolved heuristics are constructive, and decide
both which piece to pack next and where to place that
piece, given the current partial solution. This paper
contributes to a growing research area that represents
a paradigm shift in search methodologies. Instead of
using evolutionary computation to search a space of
solutions, we employ it to search a space of heuristics
for the problem. A key motivation is to investigate
methods to automate the heuristic design process. It
has been stated in the literature that humans are very
good at identifying good building blocks for solution
methods. However, the task of intelligently searching
through all of the potential combinations of these
components is better suited to a computer. With such
tools at their disposal, heuristic designers are then
free to commit more of their time to the creative
process of determining good components, while the
computer takes on some of the design process by
intelligently combining these components. This paper
shows that a GP hyper-heuristic can be employed to
automatically generate human competitive heuristics in
a very-well studied problem domain.",
uk_research_excellence_2014 = "This represents the first attempt to
use a computer to design new constructive packing
methods for rectangular stock cutting. It can
automatically produce constructive heuristics which are
often better than human-created methods. This
methodology is having a major impact in this field by
providing the foundations for fundamentally new
directions in the automatic design of effective
algorithms by computer. The results in this paper
provided some of the foundation blocks and signposts
for a new major EPSRC programme grant (EP/J017515/1) of
pounds6.8M between UCL, Stirling, York and Birmingham,
started in 2012.",