Evolving "elementary sight" strategies in predators

viaGenetic programming

ICBV Project

20.2.07

Lior Becker

Goals

Witness the evolution of the predator "strategy". Imitate the evolution of the parts in the brain that

handle the visual informal interpretation. Try to understand the development stages in the

strategy. Try to analyze the usage of the photoreceptors as

part of the brain function. Test if the development of sight strategy is a

complex process or can be emulated in a computer.

What is Genetic programming?

Bio-InspiredBio-Inspired

Inspired by Darwin’s evolutionary

principles

J.Koza style.

Charles DarwinPrinciples

CompetitionVariationOverproductionSurvival of the fittest

Population adaptation

Genetic programming

Main algorithm:1. Generate the initial population.2. Fitness evaluation.3. Create new generation:

– Selection.– Cross Over.– Mutation.

4. Repeat until stop condition.

Genetic programming Individual Representation

Individual is a Scheme-Like Function Represented as a tree (AST).

Genetic programmingRecombination - cross over

Predator strategy through GP

World simulator Predator Prey

Process of work

Prey

GP. Brain function. Undeveloped eye

15 photoreceptors. Moving ability. Fitness: catching prey.

Tree components

Function IFLTE , if less then. PLUS , add 2 num. PROGN2 , run r1 &

return r2. TL, turn right, 5 Deg. TR, turn left , 5 Deg. MF, move forward. MB, move backward.

Terminals RP, resting potential. AP, action potential. P1 .. P15,

photoreceptors , 2 Deg. MAXPP, max value of

the photoreceptors.

World simulator & Prey

WORLD

2D world. 100*100 Matrix. Predator and prey can

be at any location.

PREY

Static prey. Straight Line prey Circle prey Random prey.

Process of work

Evolving 51 generations, different preys. Test cases: unlearned preys. Plot fitness through time. Recording movies. Function analysis.

Results:

straight Line prey

Results: test case

Test Case Why is it important ?

Results: Fitness vs. generations

Improvement. population

adaptation.

Results: Function

(IFLTE (IFLTE P6 (PROGN2(IFLTE P3 P11 P13 P13 )(IFLTE P2 MAXPP MF P5 )) (PROGN2 P4 P6 )(IFLTE AP MB P5 MB )) (PLUS MAXPP P15 ) (PLUS(IFLTE P3 P1 MF P14 )(IFLTE TR MF P1 P12 )) (PROGN2(PLUS P12 P10 )(PLUS P11 TL )))

Redundancy ? – Dead code.

(IFLTE (IFLTE P6 (IFLTE P2 MAXPP MF P5) P6 (IFLTE AP MB P5 MB )) (PLUS MAXPP P15 ) (PLUS(IFLTE P3 P1 MF P14 )(IFLTE TR MF P1 P12 )) (PLUS P11 TL ))Pi – photoreceptors; TL – turn left; TR – turn right; MF – move forward.

Results: photo receptors

External spreading. Why ?? Human eye Diff.

Conclusions & discussion

1. Predator strategy evolvement.– Random strategy– Left/Right circle rotation strategy.– Combined (Left & Right) strategy.

2. External photoreceptors spared out.3. Function redundancy, The key to new life.4. None sophisticated strategies

“efficient chase”, why ?

Future work

More realistic 3D world.– Obstacles.– 3D eye– 3D world– Sophisticated preys.

Co-Evolution, prey and predator.

References

Darwin, Charles: On the origin of species by means of natural selection. London, John Murray. (1859)

John R. Koza: Genetic Programming: On the programming of computers by natural selection. MIT

Press, Cambridge, Mass. (1992) John R. Koza: Genetic Programming II: Automatic Discovery of

Reusable Programs. MIT press, Cambridge, Mass. (1994) John R. Koza: Evolution of Subsumption Using Genetic Programming.

MIT press, Cambridge, Mass. (1993) Holland, John H. Adaptation in Natural and Artificial Systems. Ann

Arbor, MI: University of Michigan Press (1975). Haynes, Sen.: Evolving behavioral strategies in predators and prey,

University of Tulsa (1996).

Movie Data Base