1. Evolving "elementary sight" strategies in predators via Genetic programming ICBV Project 20.2.07 Lior Becker

2. 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.

3. What is Genetic programming ? Bio-Inspired Bio-Inspired Inspired by Darwin’s evolutionary principles J.Koza style.

4. Charles Darwin Principles Competition Variation Overproduction Survival of the fittest Population adaptation

5. 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.

6. Genetic programming Individual Representation Individual is a Scheme-Like Function Represented as a tree (AST).

7. Genetic programming Recombination - cross over

8. Predator strategy through GP World simulator Predator Prey Process of work

9. Prey GP. Brain function. Undeveloped eye 15 photoreceptors. Moving ability. Fitness: catching prey.

10. 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.

11. 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.

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

13. Results: straight Line prey

14. Results: test case Test Case Why is it important ?

15. Results: Fitness vs. generations Improvement. population adaptation.

16. 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.

17. Results: photo receptors External spreading. Why ?? Human eye Diff.

18. 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 ?

19. Future work More realistic 3D world. – Obstacles. – 3D eye – 3D world – Sophisticated preys. Co-Evolution, prey and predator.

20. 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).

21. Movie Data Base