Geoscience Reference
In-Depth Information
8
Genetic Programming
Magic Bullet, Poisoned Chalice
or Two-Headed Monster?
Darren J. Beriro, Robert J. Abrahart and Gary Diplock
CONTENTS
Abstract .......................................................................................................................................... 169
8.1 Key Facts on Genetic Programming .................................................................................... 170
8.2 Symbolic Regression ............................................................................................................ 173
8.3 Getting the Modelling Right................................................................................................. 174
8.3.1 Stage 1 Study Site Selection ..................................................................................... 175
8.3.2 Stage 2 Data Preparation .......................................................................................... 175
8.3.3 Stage 3 Model Development ..................................................................................... 176
8.3.4 Stage 4 Rejecting and Accepting Models ................................................................. 181
8.3.5 Stage 5 Model Testing and Evaluation ..................................................................... 182
8.4 Case Studies .......................................................................................................................... 182
8.4.1 Estimating Pan Evaporation ..................................................................................... 182
8.4.2 Rainfall-Runoff Modelling ....................................................................................... 185
8.4.3 Building a Spatial Interaction Model........................................................................ 191
8.5 Future Directions .................................................................................................................. 194
8.5.1 Is It Important How Things Are Modelled? ............................................................. 195
8.5.2 Should Models Be Complex? .................................................................................... 196
8.5.3 Does It Matter What You Model? ............................................................................. 196
8.5.4 Here Be Dragons? ..................................................................................................... 197
References ...................................................................................................................................... 198
ABSTRACT
Is genetic programming (GP) a magic bullet, poisoned chalice or two-headed monster? This chapter
unpacks these questions by equipping readers with the necessary information to both productively
utilise this powerful data-driven modelling tool and to formulate some answers for themselves.
Nevertheless, navigating the GP road map can be tricky, not least because at its heart, there are
some weighty computational concepts that require suitable analogies and metaphors to make them
more easily understood - for this, its inventors piggyback onto the field of biological evolution. The
large number of borrowed terms and associated synonyms involved, however, should not in any
way discourage the discerning scientist, since GP permits fresh and novel solutions to be evolved
for resolving complex scientific problems. This chapter builds on an earlier account of GP presented
in the irst edition of this topic by discussing recent major improvements to the accessibility of the
technique, which is mainly driven by advances in computing technology and software innovation.
The first section of the current chapter provides some background to GP and its key concepts.
The central section provides a general framework for performing GP experiments, with symbolic
regression being selected as the common thread from which we weave our story. This is followed
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