Genetic Programming as a Tool for novel Creation CS 621 Seminar

1. Genetic Programming as a Tool for novel Creation CS 621 Seminar Sri Raj Paul(08305034) Course Instructor Balamurali(08405401) Prof. Pushpak Bhattacharyya

2. The way we go…. • Invention & Patent • AI & Invention • Genetic Algorithm • Genetic Programming • GP – Invention Machine • Conclusion

3. Invention • What is it? a new form, composition of matter, device, or process • What is a patent? a set of exclusive rights granted by a state to an inventor or his assignee for a fixed period of time in exchange for a disclosure of an invention

4. Is every invention Patentable? • Is an improvement over a patented invention • Result is equal to or better than a result that was placed . • Result is publishable in its own right as a new scientific result. • The result solves a problem of indisputable difficulty in its field.

5. AI & Invention • A new idea that can be logically deduced from facts that are known in a field, using transformations that are known in a field, is not considered to be inventive • Obtaining implication of given facts and rules -- Hallmark of intelligence ~ Prof. PB slides • Result: AI based on reasoning and logic cannot Invent !

6. Genetic Algorithm • Inspired by evolutionary biology • A solution represented as a chromosome • Methodology • Initialization • Selection • Reproduction • Crossover • Mutation • Termination

7. Genetic Programming • GP applies the approach of the genetic algorithm to the space of possible computer programs • Computer programs are the basic way for expressing the solutions to a wide variety of problems • Genetic programming now routinely delivers high-return human-competitive machine intelligence • High -> high AI (“artificial-to-intelligence” ) ratio • Routine -> repeating successfully on different set of problems • human-competitive -> is patentable in a sense

8. GP Operators • Reproduction • Crossing over • Mutation • Architecture Alteration operation

9. GP Flow Chart Source: John Koza slides

10. Preparatory Steps • The human user communicates the high-level statement of the problem to the genetic programming using preparatory steps Source: www.genetic-programming.com

11. Functional set and Terminal set • Alphabets of the programs to be made • The terminal set consists of the variables and constants of the programs • The functions are several mathematical functions and other more complex functions

12. FITNESS MEASURE • Specifies what needs to be done • The primary mechanism for communicating the high-level statement of the problem’s requirements • The first two preparatory steps define the search space whereas the fitness measure implicitly specifies the search’s desired goal.

13. CONTROL PARAMETERS AND TERMINATION • These steps are administrative • Control parameter: • population size. • probabilities of performing the genetic operations • the maximum size for programs • Termination criterion • maximum number of generations • may manually monitor and manually terminate • Method of designating the result • single best-so-far individual

14. GP – Invention Machine • Problem : To create a low pass filter without patent infringement of Ladder filter. • below 1,000 Hz – Pass band • above 2,000 Hz – Stop Band • Ladder Filter Source: Genetic Programming as a Darwinian Invention Machine

15. Program Architecture • Topology-modifying functions • alter the circuit topology • Component-creating functions • insert components into the circuit • Development-controlling functions • control the development process • Arithmetic-performing functions • specify the numerical value of the component • Automatically defined functions • enable certain substructures of the circuit to be reused

16. Preparatory Steps • Initial Circuit • Program Architecture • Functions • Terminals • Fitness • Control Parameters • Termination

17. Initial Circuit • Test Fixture • fixed substructure • provides access to the circuit's external input • permits probing of the circuit's output • Embryo • development occurs in the embryo Source: Genetic Programming as a Darwinian Invention Machine

18. Functions and Terminals • F= {C, L, SERIES, PARALLEL, FLIP, TVIA0, …, TVIA7, NOOP} • Tccs = {END, CUT} • ccs- construction continuing sub-tree • END makes the modifiable component with which it is associated non-modifiable • CUT causes the component to be removed from the circuit • Taps = {R} • aps- arithmetic-performing sub-tree

19. Fitness • Measurement the circuit’s behavior in the frequency domain • 101 Signals from 1 Hz and 100,000 Hz divided using a logarithmic scale is given • Error measured using Formula • Circuit’s similarity to the to-be-avoided ladder filter • sub graph of the given circuit that is matching to a sub graph of a ladder filter • Both are multiplied to get over all fitness • Smaller the overall value of fitness is better

20. Control Parameters and Termination • Control Parameters • Population size, M is 1,950,000 • Circuit constructing program tree size is 300 • Termination • Goal is to generate a variety of 100%-compliant circuits • Numerous 100%-compliant circuits were harvested • Manually terminated

21. Results • Based on Matching factor & frequency response more than 8 suitable offspring's were selected. • One of the result was elliptic filter(1927,Caur) Which is patented! Source: Genetic Programming as a Darwinian Invention Machine

22. Conclusion • GP can automatically create design that • satisfies new specification • Avoids prior art • If a suitable fitness criteria can be found ,GP can be used in any field for invention

23. Reference • J.R. Koza, F.H. Bennett III, and O. Stiffelman. 1999 Genetic Programming as a Darwinian Invention Machine. EuroGP’99, LNCS 1598, pp. 93-108, Ó Springer-Verlag Berlin Heidelberg 1999 • John R. Koza, Martin A. Keane, Matthew J. Streeter, "Routine High-Return Human-Competitive Evolvable Hardware," eh,pp.3, 2004 NASA/DoD Conference on Evolvable Hardware (EH'04), 2004 • http://www.genetic-programming.com • http://en.wikipedia.org/wiki/Genetic_programming • Prof. Pushpak Bhattacharyya slides

24. Thank You

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