Experiments in digital evolution
Artificial Life
Resilient Individuals Improve Evolutionary Search
Artificial Life
Comparing genetic robustness in generational vs. steady state evolutionary algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Dynamics of evolutionary robustness
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Simulation of the impact of retroviruses on genome organization of an artificial organism
CIBCB'09 Proceedings of the 6th Annual IEEE conference on Computational Intelligence in Bioinformatics and Computational Biology
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
The genetic coding style of digital organisms
ECAL'05 Proceedings of the 8th European conference on Advances in Artificial Life
The evolutionary emergence of intrinsic regeneration in artificial developing organisms
BioADIT'06 Proceedings of the Second international conference on Biologically Inspired Approaches to Advanced Information Technology
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We study the evolution of robustness in digital organisms adapting to a high mutation rate. As genomes adjust to the harsh mutational environment, the mean effect of single mutations decreases, up until the point where a sizable fraction (up to 30% in many cases) of the mutations are neutral. We correlate the changes in robustness along the line of descent to changes in directional epistasis, and find that increased robustness is achieved by moving from antagonistic epistasis between mutations towards codes where mutations are, on average, independent. We interpret this recoding as a breakup of linkage between vital sections of the genome, up to the point where instructions are maximally independent of each other. While such a recoding often requires sacrificing some replication speed, it is the best strategy for withstanding high rates of mutation.