Visualizing a Genetic Algorithm (GA) comes in many forms. For example, one can visualize a GA’s static or dynamic fitness landscape. Another form is of its execution. In this form, the idea is to show the dynamics of the search process. In chaos theory, an attractor is a value, or set of values, that a system settles over time. The goal of this paper to explore the visualization of mutation and crossover attractor settings for two different GA problems: the Bit Counting problem and the 0/1 Knapsack problem. Our results show that there are attractors for these two problems.