  We consider evolution of a large population, where fitness of each organism
is defined by many phenotypical traits. These traits result from expression of
many genes. We propose a new model of gene regulation, where gene expression is
controlled by a gene network with a threshold mechanism and there is a feedback
between that threshold and gene expression. We show that this regulation is
very powerful: depending on parameters we can obtain any functional connection
between thresholds and genes. Under general assumptions on fitness we prove
that such model organisms are capable, to some extent, to recognize the fitness
landscape. That fitness landscape learning sharply reduces the number of
mutations necessary for adaptation and thus accelerates of evolution. Moreover,
this learning increases phenotype robustness with respect to mutations.
However, this acceleration leads to an additional risk since learning procedure
can produce errors. Finally evolution acceleration reminds races on a rugged
highway: when you speed up, you have more chances to crash. These results
explain recent experimental data on anticipation of environment changes by some
organisms.
