MECHANICS OF GENETIC
PROGRAMMING
Tin-Shuk (Timmy) Wong
Introduction
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I am working in ECJ to use genetic programming to
develop a strategy for investing in the stock market.
Because I don’t yet have visual results, I will be
giving a brief presentation on the theory behind
genetic programming in general.
What is genetic programming?
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Genetic programming (GP) is a specialized
variation of genetic algorithms (GA). The modern
tree-based form was popularized by John Koza in
the nineties.
GP uses programs as individuals in the genetic pool,
with each program usually modeled as a binary
tree rather than a string of numbers.
Leaf nodes are typically known as terminals, while
inner nodes are known as functions.
One advantage of this representation is that the
trees can be searched and ordered easily.
Crossover
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Recall that crossover is the “reproduction” of genetic
algorithms.
GP performs crossover by randomly switching nodes
in two trees. If the nodes involved are functions,
their children will also be switched.
Another advantage of the tree structure is that even
identical trees can produce different children.
Crossover with different parents
Crossover with identical parents
Mutation
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The other primary operation used in GA and GP is
mutation.
Mutation can be implemented in a few different
ways: the GP algorithm can only allow terminals to
be altered, or it can allow entire subtrees to be
changed.
Mutation diagram
GP applied to the stock market
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Genetic programming is more powerful than
powerful than generic GA because it outputs a full
program, rather than a number. Essentially, it is
incredibly powerful at solving any problem that can
is tractable to machine learning.
It is an ideal tool for tackling the stock market, as
the market has a simple fitness criterion (earning the
most money) but has many complicated variables
that constantly change.
From here on out
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ECJ is a popular GA/GP suite developed in Java.
My strategy involves using ECJ to implement several
classes of strategies as individuals. These strategies
take historical market data as input, and their
output predictions are contrasted with recent market
data.
Hopefully, this will combine many strategies into one
improved heuristic.
Program input
Conclusion
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Any questions?
Thanks for your time.