### Multi-Objective Evolutionary Algorithms for Ecological Process Models

Abstract

Fitting an ecological process model to a set of
data is frequently done by minimizing the residual
sum of squares (RSS) between data and model output.
However, we may need to consider many component
elements of an ecological process when simulating
a model rather than just one, and the RSS may not
be an appropriate metric for simulation assessment.
For this dissertation, a multi-objective Evolutionary
Algorithm (EA) was used to fit a complex ecological
process model. As an example, a model of successive
hourly shoot growth of a forest tree was used. First,
single-objective methods were tried with the RSS;
however, the simulation results did not capture the
measured data well, especially contraction periods.
The current simulated growth is affected by that of
the previous hours because the model includes a regression
term. Thus, the fitting result could be improved if
there was information about the relation between each
data. The multi-objective optimization method allows
us to consider contraction and extension periods
separately, and these are an important phenomenon in
shoot growth. Since we can set more than one objective
function, each focused on particular data features.
Also, if there is difficulty in achieving some criteria
at the same time, analysis of differential effectiveness
in capturing contraction and extension, if it occurs,
could help to find what and where the deficiency of the
model is. These effects of considering more than one
objective function motivated using a multi-objective
optimization method. Since the model is complex, many
objective functions were required. I implemented elitism,
a process to keep the best individuals to the next
generations, but this needed to be different from that
used in other EAs and obtained the following results:
(1) crossover mutation rate should be determined
dynamically for an efficient search; (2) from analysis
of the results, deficiencies of the model were identified;
(3) with the revised model, accuracy of achievement at
contraction periods was improved; (4) the model reduced
bias, but the error did not become small; more biological
information about contraction and expansion is needed.