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Genetic Programming

Genetic Programming. CPSC 533, Artificial Intelligence. Dan Kiely Ran Shoham Brent Heigold. Agenda. What is Genetic Programming? Background/History. Why Genetic Programming? How Genetic Principles are Applied. Examples of Genetic Programs. Future of Genetic Programming.

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Genetic Programming

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  1. Genetic Programming CPSC 533, Artificial Intelligence • Dan Kiely • Ran Shoham • Brent Heigold

  2. Agenda • What is Genetic Programming? • Background/History. • Why Genetic Programming? • How Genetic Principles are Applied. • Examples of Genetic Programs. • Future of Genetic Programming.

  3. What is Genetic Programming(GP)? • Part of larger discipline called Machine Learning. Machine learning can be best described as "the study of computer algorithms that improve automatically through experience" (Mitchell 1996). • It attempts to solve the problem - How can computers be made to do what needs to be done without being told exactly how to do it? • This is where the aspect of Artificial Intelligence comes into play.

  4. Background/History • By John R. Koza, Stanford University. • 1992, Genetic Programming Treatise - “Genetic Programming. On the Programming of Computers by Means of Natural Selection.” - Origin of GP. • Combining the idea of machine learning and evolved tree structures.

  5. Why Genetic Programming? • It saves time by freeing the human from having to design complex algorithms. Not only designing the algorithms but creating ones that give optimal solutions. • Again, Artificial Intelligence.

  6. What Constitutes a Genetic Program? • Starts with "What needs to be done" • Agent figures out "How to do it" • Produces a computer program - “Breeding Programs” • Fitness Test • Code reuse • Architecture Design - Hierarchies • Produce results that are competitive with human produced results

  7. How are Genetic Principles Applied? • “Breeding” computer programs. • Crossovers. • Mutations. • Fitness testing.

  8. Computer Programs as Trees • Infix/Postfix • (2 + a)*(4 - num) * - + 2 a 4 num

  9. “Breeding” Computer Programs Hmm hmm heh. Hey butthead. Do computer programs actually score? • Start off with a large “pool” of random computer programs. • Need a way of coming up with the best solution to the problem using the programs in the “pool” • Based on the definition of the problem and criteria specified in the fitness test, mutations and crossovers are used to come up with new programs which will solve the problem.

  10. The Fitness Test • Identifying the way of evaluating how good a given computer program is at solving the problem at hand. • How good can a program cope with its environment. • Can be measured in many ways, i.e. error, distance, time, etc…

  11. Fitness Test Criteria • Time complexity a good criteria. • i.e. n2 vs. nlogn. • Accuracy - Values of variables. • Combinations of criteria may also be tested.

  12. Mutations in Nature • Ultimate source of genetic variation. • Radiation, chemicals change genetic information. • Causes new genes to be created. • One chromosome. • Asexual. • Very rare. Before: acgtactggctaa After: acatactggctaa

  13. Mutations in Programs • Single parental program is probabilistically selected from the population based on fitness. • Mutation point randomly chosen. the subtree rooted at that point is deleted, and a new subtree is grown there using the same random growth process that was used to generate the initial population. • Asexual operations are typically performed sparingly (with a low probability of, probabilistically selected from the population based on fitness).

  14. Crossovers in Nature • Two parental chromosomes exchange part of their genetic information to create new hybrid combinations (recombinant). • No loss of genes, but an exchange of genes between two previous chromosomes. • No new genes created, preexisting old ones mixed together.

  15. Crossovers in Programs • Two parental programs are selected from the population based on fitness. • A crossover point is randomly chosen in the first and second parent. • The subtree rooted at the crossover point of the first, or receiving, parent is deleted and replaced by the subtree from the second, or contributing, parent. • Crossover is the predominant operation in genetic programming (and genetic algorithm) work and is performed with a high probability (say, 85% to 90%).

  16. Examples of Genetic Programs • Symbolic Regression - the process of discovering both the functional form of a target function and all of its necessary coefficients, or at least an approximation to these. • Analog circuit design - Embryo circuit - Initial circuit which is modified to create a new circuit according to functionality criteria.

  17. Genetic Programming in the Future • Speculative. • Only been around for 8 years. • Is very successful. • Discovery of new algorithms in existing projects. Mr. Roboto

  18. Summary • Field of study in Machine Learning. • Created by John Koza in 1992. • Save time while creating better programs. • Based on the principles of genetics. • Symbolic Regression/Circuit Design. • Future uncertain.

  19. End of Show Hey Butthead. That kicked ass. Oh yeah. Hm hm yeah yeah hm. It sucked. Shut up Buttmunch. That sucked.

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