Natural selection fails to optimize mutation rates for long-term adaptation on rugged fitness landscapes

Clune J
Misevic D
Ofria C
Lenski RE
Elena SF
Sanjuán R

The rate of mutation is central to evolution. Mutations are required for adaptation, yet most mutations with phenotypic effects are deleterious. As a consequence, the mutation rate that maximizes adaptation will be some intermediate value. Here, we used digital organisms to investigate the ability of natural selection to adjust and optimize mutation rates. We assessed the optimal mutation rate by empirically determining what mutation rate produced the highest rate of adaptation. Then, we allowed mutation rates to evolve, and we evaluated the proximity to the optimum. Although we chose conditions favorable for mutation rate optimization, the evolved rates were invariably far below the optimum across a wide range of experimental parameter settings. We hypothesized that the reason that mutation rates evolved to be suboptimal was the ruggedness of fitness landscapes. To test this hypothesis, we created a simplified landscape without any fitness valleys and found that, in such conditions, populations evolved near-optimal mutation rates. In contrast, when fitness valleys were added to this simple landscape, the ability of evolving populations to find the optimal mutation rate was lost. We conclude that rugged fitness landscapes can prevent the evolution of mutation rates that are optimal for long-term adaptation. This finding has important implications for applied evolutionary research in both biological and computational realms.

Non-Adaptive Evolvability

Non-Adaptive Evolvability

Pub. Info: 
PLoS Computational Biology 4(9): e1000187

author = {Clune, Jeff and Misevic, Dusan and Ofria, Charles and Lenski, Richard E. and Elena, Santiago and Sanju\'{a}n, Rafael},
title = {Natural Selection Fails to Optimize Mutation Rates for Long-term Adaptation on Rugged Fitness Landscapes},
booktitle = {Proceedings of the 15th Annual Conference Companion on Genetic and Evolutionary Computation},
series = {GECCO '13 Companion},
year = {2013},
isbn = {978-1-4503-1964-5},
location = {Amsterdam, The Netherlands},
pages = {25--26},
numpages = {2},
url = {},
doi = {10.1145/2464576.2464597},
acmid = {2464597},
publisher = {ACM},
address = {New York, NY, USA},
keywords = {digital evolution, evolvability, meta-GA, self-adaptive},