Alpha is simply a parameter that controls the rate of fall-off in the fitness function. It is a constant between 0 and 1. The smaller alpha is, the faster the fall-off of fitness with error.
Epsilon is the error of a classifier. It is a recency-weighted average of the absolute difference between the classifier's prediction of payoff and the actual payoff received. See Section 3.2.
Epsilon0 is a constant. A classifier whose epsilon is less than epsilon0 has, by definition, accuracy 1.0. For epsilon greater than epsilon0, the accuracy is given by the exponential expression of this section. For the broader significance of epsilon0, see the last paragraph of Section 4.1.
What are alpha, epsilon, and epsilon0 ? Can't find them defined in the paper.