Working with Evolutionary Algorithms. Chapter 14. Issues considered. Experiment design Algorithm design Test problems Measurements and statistics Some tips and summary. Experimentation . Has a goal or goals Involves algorithm design and implementation
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find a very good solution at least once
find a good solution at almost every run
must meet scientific standards (huh?)
good enough is good enough (verification!)
These perspectives have very different implications on evaluating the results (yet often left implicit)
Choice has severe implications on
never draw any conclusion from a single run
always do a fair competition
Many different ways. Examples:
= no. of fitness evaluations
(don’t use no. of generations!)
Populations mean (best) fitness
Which algorithm is better? Why? When?
Averaging can “choke” interesting information
Overlay of curves can lead to very “cloudy” figures
Is the new method better?
(AES for benchmark?)
(with special attention to the effect of n, as for scale-up)