| Target Function | y = A sin(Ax) |
| Fitness samples | 200, domain is randomly chosen from [-1, +1)
Regenerated after every generation |
| Population Size | 1000 |
| Generations | 1000 |
| Operations | Crossover (90%)
Subtree Mutation (10%) Reproduction (5%) Reproduction of best, 2nd best, etc... from last generation (5%) |
| Limits | Depth <= 17 |
| Selection | Generational, Tournament (size = 7) |
| Terminals and non-terminals | sin, cos, rlog, exp, +, -, *, /, random constant from (-1..1) (common)
X, ADF0 (RPB only) |
The value of A was perturbed either every generation, or every 5 generations. The space of time between perturbations of A is an epoch. Thus an epoch is either 1 or 5 generations long. Measurements at the end of an epoch occurred at the end of a generation, and just before A is perturbed. Measurements at the start of an epoch occurred immediately after A was perturbed, and before any evolution could occur. In either case, I measured the best of generation, and averaged over a number of runs.
Perturbation of A was either brownian or uniform. If it was brownian, then it started with A = 3, and was perturbed by adding a uniform random value from the range [-1, +1]. If it was uniform, then A again started at 3, and was perturbed by setting it to a new value from the range [-3, 9].
Fitness is measured in the number of hits (sample points where the result
was within 0.1 of correct).
Fitness difference is the fitness at the end of an epoch minus the
fitness at the beginning of that same epoch. Thus, positive values
of fitness difference indicate the degree of evolvability of the population
at that point.
Tree sizes are measured in the average number of nodes contained in
the best of population.
I measure constancy by rounding all the values returned by ADF0 to the nearest 0.01 and then counting the unique values. Thus, a purely constant ADF0 returns a value of 1.
Fitness
Fitness Differences
Tree Sizes
Constancy of ADF0
Fitness
Fitness Differences
Tree Sizes