Mathematical Modelling of Natural Selection
Mathematical and computational modeling of evolution is a huge field. To start you off with some broad strokes, there are two main approaches to evolutionary modeling with their own communities: frequency-independent and frequency-dependent models. Of course, in a real biological setting, the truth lies somewhere in between, but models are idealizations of key principles, and so the two extremes are good to study.
For frequency-independent selection, they key concept is the fitness landscape -- a way to map each genotype to a fitness. The population then lives in this landscape and agents with higher fitness reproduce better and so the population slowly movers over the lattice. Under reasonable macroevolutionary assumptions such as very rare mutations and asexual populations, the population will tend to be homogenous and can be just modeled as a single point in the fitness landscape with properly chosen selective sweeps to move the point from vertex to vertx (Gillespie, 1983; 1984).
The most popular concrete approach for frequency-independent selection is [Kauffman's NK model of rugged fitness landscapes] (Kauffman &amp; Weinberger, 1989; Kauffman, 1993). Computer simulations tend to favor this sort of model. Wilf's model would be an NK model with K = 0, and fitness of 0 for incorrect letters and 1 for correct letters (to get the shorter walk). Related questions:

Is local equilibrium a reasonable assumption for evolutionary processes?
Structure of fitness landscapes in the NK model

The frequency-dependent selection approach is dominated by evolutionary game theory. A typical question for this field is the evolution of cooperation, and the most basic approach is to use replicator dynamics and look at evolutionary stable strategies. However, the field is in general saturated with all kinds of exciting models (both analytic and computational). A very friendly book-length intro is Nowak's Evolutionary Dynamics. For a genetle intro and connections to evolutionary psychology look at the slides in this post. A brief and sophisticated introduction aimed at mathematicians can be found in Hofbauer &amp; Sigmund (2003). Related questions:

Sources for Algorithmic Evolutionary Game Theory
Why is &#39;Grudger&#39; an evolutionary stable strategy?
How exactly are game theoretical evolutionary models described during implementation for computer simulations?
When does weak selection produce qualitatively different results from strong selection?

References
Gillespie, J.H. (1983). A simple stochastic gene substitution model. Theor. Pop. Biol. 23, 202.
Gillespie, J.H. (1984). Molecular evolution over the mutational landscape. Evolution 38, 1116.
Hofbauer, J., &amp; Sigmund, K. (2003). Evolutionary game dynamics. Bulletin of the American Mathematical Society, 40(4), 479-519.
Kauffman, S. (1993). The origins of order: Self organization and selection in evolution. Oxford University Press, USA.
Kauffman, S. &amp; Weinberger, E. (1989) The NK Model of rugged fitness landscapes and its application to the maturation of the immune response. Journal of Theoretical Biology, 141(2): 211-245
Nowak, M. A. (2006). Evolutionary dynamics: exploring the equations of life. Harvard University Press.