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Mechanics of Genetic Programming

Mechanics of Genetic Programming. Tin-Shuk (Timmy) Wong. Introduction. I am working in ECJ to use genetic programming to develop a strategy for investing in the stock market.

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Mechanics of Genetic Programming

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  1. Mechanics of Genetic Programming Tin-Shuk (Timmy) Wong

  2. Introduction • 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.

  3. What is genetic programming? • 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.

  4. Crossover • 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.

  5. Crossover with different parents

  6. Crossover with identical parents

  7. Mutation • 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.

  8. Mutation diagram

  9. GP applied to the stock market • 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.

  10. From here on out • 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.

  11. Program input

  12. Conclusion • Any questions? • Thanks for your time.

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