title: Evolution of evolvability via adaptation of mutation rates.
abstract: We examine a simple form of the evolution of evolvability-the evolution of mutation rates-in a simple model system. The system is composed of many agents moving, reproducing, and dying in a two-dimensional resource-limited world. We first examine various macroscopic quantities (three types of genetic diversity, a measure of population fitness, and a measure of evolutionary activity) as a function of fixed mutation rates. The results suggest that (i) mutation rate is a control parameter that governs a transition between two qualitatively different phases of evolution, an ordered phase characterized by punctuated equilibria of diversity, and a disordered phase of characterized by noisy fluctuations around an equilibrium diversity, and (ii) the ability of evolution to create adaptive structure is maximized when the mutation rate is just below the transition between these two phases of evolution. We hypothesize that this transition occurs when the demands for evolutionary memory and evolutionary novelty are typically balanced. We next allow the mutation rate itself to evolve, and we observe that evolving mutation rates adapt to values at this transition. Furthermore, the mutation rates adapt up (or down) as the evolutionary demands for novelty (or memory) increase, thus supporting the balance hypothesis.
authors.affiliation: Reed College, 3203 SE Woodstock Blvd., Portland, OR 97202, USA. mab@reed.edu
authors.forename: Mark A
authors.initials: MA
authors.lastname: Bedau
authors.forename: Norman H
authors.initials: NH
authors.lastname: Packard
mesh_terms: D000222:Adaptation, Physiological / Q000502:physiology*; D000818:Animals; D005075:Biological Evolution; D003198:Computer Simulation; D004252:DNA Mutational Analysis / Q000379:methods*; D005787:Gene Frequency / Q000235:genetics; D014644:Genetic Variation / Q000235:genetics*; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D009154:Mutation / Q000235:genetics*; D011157:Population Dynamics; D012641:Selection, Genetic; D013269:Stochastic Processes