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Assignment #5 – Genetic Programming – Sensitivity of Parameters [Population Size]
Genetic Programming (GP) is an evolutionary method that is adept at solving optimal instruction
set problems. Some examples of problems that GP is well suited for are: computer programs,
logic circuits, and mathematical equations.
Genetic programming represents a population of candidate solutions as trees. These trees are
composed of elements from a predetermined function set and terminal set. The members of the
function set will have some number of operands which can be satisfied by members of both the
function set and the terminal set. New solution populations are evolved from these population
members using genetic operators such as crossover [where sub-trees are exchanged between
two parent solutions] and mutation [where a sub-tree of a solution is randomly modified to create
a new candidate solution].
One critical issue with GP design is the selection of the population size. While a large population
will ensure that there is a sufficient diversity of solutions, this can also increase the computational
cost of evaluating the fitness of the population.
You must run a series of experiments that can assess the impact population size has on the
convergence of a genetic programming system.
Your report must address:

What genetic programming is and how it works

How you organized your experiments and why they should provide credible evidence that
can assess the impact population size has on the convergence of a genetic programming
system

What are your results and why they are important
Your research report is due in two weeks.
References:
Banzhaf, Wolfgang, Nordin, Peter, Keller, Robert E., and Francone Frank D. Genetic
Programming - An Introduction; On the Automatic Evolution of Computer Programs and its
Applications. Morgan Kaufmann Publishers, Inc. 1998
Satry, Kumara, O’Reilly, Una-May, Goldberg, David, Hill, David, Building Block Supply in
Genetic Programming, IlliGAL Report No. 2003012, April 2003