Ruggedness and neutrality—the NKp family of fitness landscapes
ALIFE Proceedings of the sixth international conference on Artificial life
Chance and necessity in evolution: lessons from RNA
Physica D - Special issue originating from the 18th Annual International Conference of the Center for Nonlinear Studies, Los Alamos, NM, May 11&mdash ;15, 1998
Fitness landscapes and evolvability
Evolutionary Computation
Through the Labyrinth Evolution Finds a Way: A Silicon Ridge
ICES '96 Proceedings of the First International Conference on Evolvable Systems: From Biology to Hardware
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Combating Coevolutionary Disengagement by Reducing Parasite Virulence
Evolutionary Computation
A study of mutational robustness as the product of evolutionary computation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
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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.