Evolutionary Algorithms - GeoComputation - page 163

Methodologically speaking, one of the greatest success stories of the past 10 years in GC has
been the appearance and establishment of high-resolution geographical modelling, for example,
ABM (see Benenson 2014). One could speculate that this has been to the detriment of research
into EAs, that is, researchers have shifted their attention into the design and construction of these
models. These new geographical models are allowing researchers to construct complicated models
at an individual level from the bottom up. However, we cannot be completely sure that we have the
workings correctly designed (correct parameter values or rules) until we run the models. These
methodologies are now reaching a point of maturity when in-depth work is being undertaken into
important areas such as understanding how error propagates in these systems (Evans 2012) and how
best to calibrate and validate these increasingly complicated models (see Heppenstall et al. 2012).
EAs potentially hold the key to allowing researchers to optimise and breed better solutions and rules
for these models. As Diplock commented in 2000, 'There is much to gain from the adoption of new
and innovative approaches to geographical research, and little to lose' (p. 250).
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