The Australian River Assessment System (AusRivAS) is a nation-wide programme designed to assess the health of Australian rivers and streams. In order to produce river health assessments, the AusRivAS method uses the outcomes of cluster analysis applied to macroinvertebrate data from a number of different locations. At present, the clustering step is conducted using the statistical Unweighted Pair Group Arithmetic Averaging (UPGMA) method. A potential shortcoming of this approach is that it uses a linear performance measure for grouping similar data points. A recently developed approach for clustering ecological data (MIR-max) overcomes this limitation by using mutual information as the performance measure. However, MIR-max uses a hill-climbing approach for optimising mutual information, which could become trapped in local optima of the search space. In this paper, the potential of using evolutionary algorithms (EAs), such as genetic algorithms and ant colony optimisation algorithms, for maximising the mutual information of ecological data clusters is investigated. The MIR-max and EA-based approaches are applied to the South Australian combined season riffle AusRivAS data, and the results obtained are compared with those obtained using the UPGMA method. The results indicate that the overall mutual information values of the clusters obtained using MIR-max and the EA-based approaches are significantly higher than those obtained using the UPGMA method, and that the use of genetic and ant colony optimisation algorithms is successful in determining clusters with higher overall mutual information values compared with those obtained using MIR-max for the case study considered.
