Genetic Programming as a Tool for novel Creation

CS 621 Seminar

Sri Raj Paul(08305034) Course InstructorBalamurali(08405401) Prof. Pushpak Bhattacharyya

THE WAY WE GO…. Invention & Patent

AI & Invention

Genetic Algorithm

Genetic Programming

GP – Invention Machine

Conclusion

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

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.

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 !

GENETIC ALGORITHM• Inspired by evolutionary biology• A solution represented as a chromosome• Methodology

– Initialization– Selection– Reproduction

• Crossover • Mutation

– Termination

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

GP OPERATORS Reproduction Crossing over Mutation Architecture Alteration operation

GP FLOW CHART

Source: John Koza slides

PREPARATORY STEPS

Source: www.genetic-programming.com

• The human user communicates the high-level statement of the problem to the genetic programming using preparatory steps

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

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.

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

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

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

PREPARATORY STEPS Initial Circuit Program Architecture Functions Terminals Fitness Control Parameters Termination

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

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

FITNESS1. 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 filter3. Both are multiplied to get over all fitness4. Smaller the overall value of fitness is better

))()),((()(100

0ii

ii fdffdWtF

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

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

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

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

Thank You

Source: www.genetic-programming.com

Next >>

Source: www.genetic-programming.com

Next >>

Source: www.genetic-programming.com

Next >>