Abstract
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. Under some 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 needed for adaptation.Moreover, this learning increases phenotype robustness with respect to mutations, i.e., canalizes the phenotype. We show that learning and canalization work only when evolution is gradual. Organisms can be adapted to many constraints associated with a hard environment, if that environment becomes harder step by step.Our results explain why evolution can involve genetic changes of a relatively large effect and why the total number of changes are surprisingly small.
Highlights
A central idea of modern biology is that evolution proceeds by mutation and selection
There is a limited amount of experimental support for this idea2 and some experimental evidence that evolution can involve genetic changes of a relatively large effect and that the total number of changes are surprisingly small3
5 Discussion In this paper, we proposed a model for fitness landscape learning, which extends earlier work by 7–9 in two ways
Summary
A central idea of modern biology is that evolution proceeds by mutation and selection. There is a limited amount of experimental support for this idea and some experimental evidence that evolution can involve genetic changes of a relatively large effect and that the total number of changes are surprisingly small3 Another intriguing fact is that organisms are capable of making adaptive predictions of environmental changes. Under some conditions— weak selection, see 10—a polynomially large population over polynomially many generations (polynomial in N ) will end g up almost surely consisting exclusively of assignments, which satisfy all constraints This theorem can shed light on the problem of the evolution of complex adaptations since that satisfiability problem can be considered as a rough mathematical model of adaptation to many constraints. Learning can sharply reduce the number of mutations needed to form a phenotypic trait useful for adaptation that is consistent with experimental data mentioned above Correspondence i between Boolean hypercube and genotypes is considered for example in 12
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