Publications

Analytical models show that high-dimensional fitness landscapes form “holey” rather than “rugged” topographies, but the implications of this finding for biological and artificial life systems remain largely unexplored. One of the reasons for this gap can be attributed to serious difficulties in the implementation of individual-based holey fitness landscape (HFL) models. Here, we introduce a method for simulating HFLs in spatially explicit individual-based models that overcomes these difficulties. We examine how the HFL changes predictions for the maintenance of genetic diversity in the face of migration. Previous models suggest that ecologically-based reproductive isolation will rapidly collapse under migration. Our results indicate that an underlying HFL can often maintain diversity in this situation. Hybrid species emerge frequently when HFL genetics are simulated, but are usually doomed to extinction because of small population sizes. However, hybridisation can also lead to novel adaptations and potentially the exploitation of new ecological niches. More generally, the results imply that HFL genetics should not be neglected in studies of adaptation and diversity.

Premature convergence is a persisting problem in evolutionary optimisation, in particular – genetic algorithms. While a number of methods exist to approach this issue, they usually require problem specific calibration or only partially resolve the issue, at best by delaying the premature convergence of an evolving population. Analytical models in biology show that resiliently diverse populations evolve on high-dimensional fitness landscapes with “holey” rather than “rugged” topographies, but the implications for artificial evolutionary systems remain largely unexplored. Here I show how holey fitness landscapes (HFLs) can be incorporated in an evolutionary algorithm and use this approach to investigate the ability of HFLs to maintain genetic diversity in an evolving population. The results indicate that an underlying HFL can counteract premature genetic convergence and sustain diversity. They also suggest that HFL may provide a flexible mechanism for dynamic creation and maintenance of subpopulations that concentrate their evolutionary search in different regions of the solution space. Finally, I discuss on-going work on using the HFL model in optimisation problems.

We provide a brief overview of the use of FLs in evolutionary biology and introduce an FL model suitable for individual-based models of species evolution. Our model combines different features of several analytic FL models used in evolutionary biology. Our new model overcomes several difficulties encountered with previous FL models, particularly arbitrary divergence thresholds. We discuss the difficulties encountered during the implementation of our model and how we have overcome these. We present a detailed numerical analysis of the proposed family of FLs that will inform future work based on our FL model.