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

2. T HE WAY WE GO …. Invention & Patent AI & Invention Genetic Algorithm Genetic Programming GP – Invention Machine Conclusion

3. I NVENTION 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. I S EVERY INVENTION P ATENTABLE ? 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 & I NVENTION 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. G ENETIC A LGORITHM Inspired by evolutionary biology A solution represented as a chromosome Methodology – Initialization – Selection – Reproduction Crossover Mutation – Termination

7. G ENETIC P ROGRAMMING 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 O PERATORS Reproduction Crossing over Mutation Architecture Alteration operation

9. GP F LOW C HART Source: John Koza slides

10. P REPARATORY S TEPS Source: www.genetic-programming.com The human user communicates the high-level statement of the problem to the genetic programming using preparatory steps

11. F UNCTIONAL SET AND T ERMINAL 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 – I NVENTION M ACHINE 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. P ROGRAM A RCHITECTURE 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. P REPARATORY S TEPS Initial Circuit Program Architecture Functions Terminals Fitness Control Parameters Termination

17. I NITIAL C IRCUIT 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. F UNCTIONS AND T ERMINALS F = {C, L, SERIES, PARALLEL, FLIP, TVIA0, …, TVIA7, NOOP} T ccs = {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 T aps = {R} – aps- arithmetic-performing sub-tree

19. F ITNESS 1. 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 2. 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 3. Both are multiplied to get over all fitness 4. Smaller the overall value of fitness is better

20. C ONTROL P ARAMETERS AND T ERMINATION 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. R ESULTS 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. C ONCLUSION 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. R EFERENCE 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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