An Introduction to Genetic Algorithms - Boente

6.
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c.
d.
a.
b.
Chapter 1: Genetic Algorithms: An Overview
Turn off crossover (set pc = 0) and see how this affects the average best fitness reached and
the average number of generations to reach the best fitness. Before doing these experiments, it
might be helpful to read Axelrod 1987.
Try varying the amount of memory of strategies in the population. For example, try a version
in which each strategy remembers the four previous turns with each other player. How does
this affect the GA's performance in finding high−quality strategies? (This is for the very
ambitious.)
See what happens when noise is added—i.e., when on each move each strategy has a small
probability (e.g., 0.05) of giving the opposite of its intended answer. What kind of strategies
evolve in this case? (This is for the even more ambitious.)
Implement a GA to search for strategies to play the Iterated Prisoner's Dilemma as in
computer exercise 5a, except now let the fitness of a strategy be its score in 100 games with
TIT FOR TAT. Can the GA evolve strategies to beat TIT FOR TAT?
Compare the GA's performance on finding strategies for the Iterated Prisoner's Dilemma with
that of steepest−ascent hill climbing and with that of random−mutation hill climbing. Iterate
the hill−climbing algorithms for 1000 steps (fitness−function evaluations). This is equal to the
number of fitness−function evaluations performed by a GA with population size 20 run for 50
generations. Do an analysis similar to that described in computer exercise 4.
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