Guttenberg Nicholas, Tabei S M Ali, Dinner Aaron R
James Franck Institute, The University of Chicago, Chicago, Illinois 60637, USA.
Phys Rev E Stat Nonlin Soft Matter Phys. 2011 Sep;84(3 Pt 1):031932. doi: 10.1103/PhysRevE.84.031932. Epub 2011 Sep 30.
We exploit a simple model to numerically and analytically investigate the effect of enforcing a time constraint for achieving a system-wide goal during an evolutionary dynamics. This situation is relevant to finding antibody specificities in the adaptive immune response as well as to artificial situations in which an evolutionary dynamics is used to generate a desired capability in a limited number of generations. When the likelihood of finding the target phenotype is low, we find that the optimal mutation rate can exceed the error threshold, in contrast to conventional evolutionary dynamics. We also show how a logarithmic correction to the usual inverse scaling of population size with mutation rate arises. Implications for natural and artificial evolutionary situations are discussed.
我们利用一个简单模型，通过数值和解析方法研究在进化动力学过程中施加时间限制以实现全系统目标的效果。这种情况既与在适应性免疫反应中寻找抗体特异性相关，也与利用进化动力学在有限代数内产生所需能力的人工情况相关。当找到目标表型的可能性较低时，我们发现与传统进化动力学不同，最优突变率可能超过错误阈值。我们还展示了通常的种群大小与突变率的反比缩放如何出现对数修正。讨论了对自然和人工进化情况的影响。
