Encouraging behavioral diversity in evolutionary robotics: An empirical study

Evolutionary Computation

Volumn 20, Issue 1, 2012, Pages 91-133

  Mouret, J B ^a   Doncieux, S ^a  

^a UNIVERSITÉ PIERRE ET MARIE CURIE   (France)

Author keywords

Diversity; Evolutionary robotics; Multi objective evolutionary algorithm; Neural networks

Indexed keywords

BEHAVIORAL DIVERSITY; DIVERSITY; EMPIRICAL STUDIES; EVOLUTIONARY OPTIMIZATIONS; EVOLUTIONARY PROCESS; EVOLUTIONARY ROBOTICS; FITNESS FUNCTIONS; INFINITE NUMBERS; MULTI OBJECTIVE; MULTI-OBJECTIVE EVOLUTIONARY ALGORITHM; PREMATURE CONVERGENCE; ROBOT BEHAVIOR;

EVOLUTIONARY ALGORITHMS; NEURAL NETWORKS;

HAMMING DISTANCE;

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; BEHAVIOR; COMPUTER AIDED DESIGN; COMPUTER SIMULATION; METHODOLOGY; ROBOTICS; THEORETICAL MODEL;

ALGORITHMS; BEHAVIOR; COMPUTER SIMULATION; COMPUTER-AIDED DESIGN; MODELS, THEORETICAL; NEURAL NETWORKS (COMPUTER); ROBOTICS;

EID: 84857509059     PISSN: 10636560     EISSN: 15309304     Source Type: Journal    
DOI: 10.1162/EVCO_a_00048     Document Type: Review

References (56)

1. Abbass, H. A., and Deb, K. (2003). Searching under multi-evolutionary pressures. In EMO'03: Proceedings of the 2nd International Conference on Evolutionary Multi-Criterion Optimization. Lecture Notes in Computer Science, Vol. 2632 (pp. 391-404). Berlin: Springer-Verlag.
2. Aho, A. V., Garey, M. R., and Ullman, J. D. (1972). The transitive reduction of a directed graph. SIAM Journal on Computing, 1:131-137.
3. Barrero, D. F., Camacho, D., and Rodriguez-Moreno, M. D. (2010). Confidence intervals of success rates in evolutionary computation. In GECCO '10: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, pp. 975-976.
4. Birattari, M., Stützle, T., Paquete, L., and Varrentrapp, K. (2002). A racing algorithm for configuring metaheuristics. In GECCO'02: Proceedings of the 4th Annual Genetic and Evolutionary Computation Conference, pp. 11-18.
5. Bongard, J. C., and Hornby, G. S. (2010). Guarding against premature convergence while accelerating evolutionary search. In GECCO'10: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, pp. 111-118.
6. Bui, L., Branke, J., and Abbass, H. (2005). Diversity as a selection pressure in dynamic environments. In GECCO'05: Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation, pp. 1557-1558.
7. Bunke, H., and Shearer, K. (1998). A graph distance metric based on the maximal common subgraph. Pattern Recognition Letters, 19(3):255-259.
8. Darwen, P., and Yao, X. (1996). Every niching method has its niche: Fitness sharing and implicit sharing compared. In Parallel Problem Solving from Nature, PPSN IV. Lecture Notes in Computer Science, Vol. 1141 (pp. 398-407). Berlin: Springer-Verlag.
9. De Jong, E. D., Watson, R. A., and Pollack, J. B. (2001). Reducing bloat and promoting diversity using multi-objective methods. In GECCO'01: Proceedings of the 3rd Annual Conference on Genetic and Evolutionary Computation, pp. 11-18.
10. Deb, K. (2001). Multi-objectives optimization using evolutionary algorithms. New York:Wiley.
11. Deb, K., Agrawal, S., Pratab, A., and Meyarivan, T. (2000). A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In Parallel Problem Solving from Nature, PPSN VI. Lecture Notes in Computer Science, Vol. 1917 (pp. 849-858). Berlin: Springer-Verlag.
12. Doncieux, S., and Meyer, J.-A. (2003). Evolving neural networks for the control of a lenticular blimp. In Applications of Evolutionary Computing, EvoWorkshops2003: EvoBIO, EvoCOP, EvoIASP, EvoMUSART, EvoROB, EvoSTIM. Lecture Notes in Computer Science, Vol. 2611 (pp. 626-637). Berlin: Springer-Verlag.
13. Doncieux, S., and Mouret, J.-B. (2009). Single step evolution of robot controllers for sequential tasks. In GECCO '09: Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, pp. 1771-1772.
14. Doncieux, S., and Mouret, J.-B. (2010). Behavioral diversity measures for evolutionary robotics. In IEEE Congress on Evolutionary Computation (CEC), pp. 1303-1310.
15. Doncieux, S., Mouret, J.-B., and Bredeche, N. (2009). Evolutionary robotics: Exploring new horizons. In New Horizons in Evolutionary Robotics: Extended Contributions from the 2009 EvoDeRob Workshop. Studies in Computational Intelligence, Vol. 341 (pp. 3-25). Berlin: Springer-Verlag.
16. Elman, J. L. (1993). Learning and development in neural networks: The importance of starting small. Cognition, 48(1):71-99.
17. Floreano, D., Dürr, P., and Mattiussi, C. (2008). Neuroevolution: From architectures to learning. Evolutionary Intelligence, 1(1):47-62.
18. Floreano, D., and Mondada, F. (1994). Automatic creation of an autonomous agent: Genetic evolution of a neural-network driven robot. In From Animals to Animats 3: Proceedings of the 3rd International Conference on Simulation of Adaptive Behavior (SAB'94), pp. 421-430.
19. Fogel, L. G., Owens, A. J., andWalsh, M. J. (1966). Artificial intelligence through simulated evolution. New York:Wiley.
20. Friedrich, T., Oliveto, P. S., Sudholt, D., and Witt, C. (2008). Theoretical analysis of diversity mechanisms for global exploration. In GECCO'08: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, pp. 945-952.
21. Goldberg, D. E. (1987). Simple genetic algorithms and the minimal, deceptive problem. In Genetic algorithms and simulated annealing (pp. 74-88). San Mateo, CA: Morgan Kaufmann.
22. Goldberg, D. E., Deb, K., and Horn, J. (1992). Massive multimodality, deception, and genetic algorithms. In Parallel Problem Solving from Nature, PPSN II, pp. 37-46.
23. Gomez, F. J. (2009). Sustaining diversity using behavioral information distance. In GECCO'09: Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, pp. 113-120.
24. Gruau, F. (1995). Automatic definition of modular neural networks. Adaptive Behaviour, 3(2):151-183.
25. Harvey, I., Husbands, P., and Cliff, D. (1994). Seeing the light: Artificial evolution; Real vision. In From Animals to Animats 3: Proceedings of the Third International Conference on Simulation of Adaptive Behavior, pp. 392-401.
26. Holland, J. (1992). Adaptation in natural and artificial systems. Cambridge, MA: MIT Press.
27. Hornby, G. S., and Pollack, J. B. (2002). Creating high-level components with a generative representation for body-brain evolution. Artificial Life, 8(3):223-246.
28. Knowles, J., Watson, R., and Corne, D. (2001). Reducing local optima in single-objective problems by multi-objectivization. In EMO'01: Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization. Lecture Notes in Computer Science, Vol. 1993 (pp. 269-283). Berlin: Springer-Verlag.
29. Kodjabachian, J., and Meyer, J.-A. (1997). Evolution and development of neural networks controlling locomotion, gradient-following, and obstacle-avoidance in artificial insects. IEEE Transactions on Neural Networks, 9(5):796-812.
30. Lehman, J., and Stanley, K. O. (2008). Exploiting open-endedness to solve problems through the search for novelty. In Proceedings of Artificial Life XI, pp. 329-336.
31. Lehman, J., and Stanley, K. O. (2010). Abandoning objectives: Evolution through the search for novelty alone. Evolutionary Computation, 19(2):189-223.
32. Lipson, H. (2005). Evolutionary robotics and open-ended design automation. Biomimetics, 17(9):129-155.
33. Lipson, H., and Pollack, J. (2000). Automatic design andmanufacture of robotic lifeforms. Nature, 406:974-978.
34. Lopresti, D., and Wilfong, G. (2003). A fast technique for comparing graph representations with applications to performance evaluation. International Journal on Document Analysis and Recognition, 6(4):219-229.
35. Mahfoud, S. (1997). Niching methods. In T. Back, D. B. Fogel, and Z. Michalewicz (Eds.), Handbook of evolutionary computation (pp. C6.1:1-C6.1:4). Oxford, UK: Taylor & Francis.
36. Mauldin, M. (1984). Maintaining diversity in genetic search. In Proceedings of the National Conference on Artificial Intelligence, pp. 247-250.
37. Meyer, J.-A., Husbands, P., and Harvey, I. (1998). Evolutionary robotics: A survey of applications and problems. In Proceedings of The First European Workshop on Evolutionary Robotics, EvoRobot98, pp. 1-21.
38. Moriguchi, H., and Honiden, S. (2010). Sustaining behavioral diversity in NEAT. In GECCO'10: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, pp. 611-618.
39. Mouret, J.-B. (2011). Novelty-based multiobjectivization. In New Horizons in Evolutionary Robotics: Extended Contributions from the 2009 EvoDeRobWorkshop. Studies in Computational Intelligence, Vol. 341 (pp. 139-154). Berlin: Springer-Verlag.
40. Mouret, J.-B., and Doncieux, S. (2008). Incremental evolution of animats' behaviors as a multiobjective optimization. In From Animals to Animats 10: Proceedings of the Tenth International Conference on Simulation of Adaptive Behavior (SAB2008). Lecture Notes in Artificial Intelligence, Vol. 5040 (pp. 210-219). Berlin: Springer-Verlag.
41. Mouret, J.-B., and Doncieux, S. (2009a). Overcoming the bootstrap problem in evolutionary robotics using behavioral diversity. In IEEE Congress on Evolutionary Computation (CEC), pp. 1161-1168.
42. Mouret, J.-B., and Doncieux, S. (2009b). Using behavioral exploration objectives to solve deceptive problems in neuro-evolution. In GECCO'09: Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, pp. 627-634.
43. Mouret, J.-B., and Doncieux, S. (2010). Sferes v2: Evolving in the multi-core world. In IEEE Congress on Evolutionary Computation (CEC), pp. 4079-4086.
44. Mouret, J.-B., Doncieux, S., and Meyer, J.-A. (2006). Incremental evolution of target-following neuro-controllers for flapping-wing animats. In From Animals to Animats 9: Proceedings of the 9th International Conference on the Simulation of Adaptive Behavior (SAB2006). Lecture Notes in Artificial Intelligence, Vol. 4095 (pp. 210-219). Berlin: Springer-Verlag.
45. Nolfi, S., and Floreano, D. (2004). Evolutionary robotics: The biology, intelligence, and technology of self-organizing machines. Cambridge, MA: MIT Press.
46. Risi, S., Vanderbleek, S. D., Hughes, C. E., and Stanley, K. O. (2009). How novelty search escapes the deceptive trap of learning to learn. In GECCO'09: Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, pp. 153-160.
47. Sareni, B., and Krahenbuhl, L. (1998). Fitness sharing and niching methods revisited. IEEE Transactions on Evolutionary Computation, 2(3):97-106.
48. Schwefel, H. P. (1984). Evolution strategies: A family of non-linear optimization techniques based on imitating some principles of organic evolution. Annals of Operations Research, 1(2):165-167.
49. Smit, S. K., and Eiben, A. E. (2009). Comparing parameter tuning methods for evolutionary algorithms. In IEEE Congress on Evolutionary Computation (CEC), pp. 399-406.
50. Stanley, K. O., and Miikkulainen, R. (2002). Evolving neural networks through augmenting topologies. Evolutionary Computation, 10(2):99-127.
51. Toffolo, A., and Benini, E. (2003). Genetic diversity as an objective in multi-objective evolutionary algorithms. Evolutionary Computation, 11(2):151-167.
52. Trujillo, L., Olague, G., Lutton, E., and De Vega, F. F. (2008a). Behavior-based speciation for evolutionary robotics. In GECCO'08: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, pp. 297-298.
53. Trujillo, L., Olague, G., Lutton, E., and De Vega, F. F. (2008b). Discovering several robot behaviors through speciation. In Application of Evolutionary Computing: 4th European Workshop on Bio-Inspired Heuristics for Design Automation. Lecture Notes in Computer Science, Vol. 2279 (pp. 165-174). Berlin: Springer-Verlag.
54. Walker, J., Garrett, S., andWilson, M. (2003). Evolving controllers for real robots: A survey of the literature. Adaptive Behavior, 11(3):179-203.
55. Yuan, B., and Gallagher, M. (2009). An improved small-sample statistical test for comparing the success rates of evolutionary algorithms. In GECCO'09: Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, pp. 1879-1880.
56. Zhang, K., Statman, R., and Shasha, D. (1992). On the editing distance between unordered labeled trees. Information Processing Letters, 42(3):133-139.