As a population evolves, its members are under selection both for rate of reproduction (fitness) and mutational robustness. For those using evolutionary algorithms as optimisation techniques, this second selection pressure can sometimes be beneficial, but it can also bias evolution in unwelcome and unexpected ways. Here, the role of selection for mutational robustness in driving adaptation on neutral networks is explored. The behaviour of a standard genetic algorithm is compared with that of a search algorithm designed to be immune to selection for mutational robustness. Performance on an RNA folding landscape suggests that selection for mutational robustness, at least sometimes, will not unduly retard the rate of evolutionary innovation enjoyed by a genetic algorithm. Two classes of random landscape are used to explore the reasons for this result.
|Title of host publication||Artificial Life VIII: Proceedings of the Eighth International Conference on the Synthesis and Simulation of Living Systems|
|Editors||Russel K. Standish, Mark Bedau, Hussein Abbass|
|Publisher||Massachusetts Institute of Technology (MIT) Press|
|Number of pages||10|
|Publication status||Published - 2002|