Evolution of swarm robotics systems with novelty search

Swarm Intelligence

Volumn 7, Issue 2-3, 2013, Pages 115-144

  Gomes, Jorge ^a   Urbano, Paulo ^a   Christensen, Anders Lyhne ^b  

^a UNIVERSITY OF LISBON   (Portugal)

^b INSTITUTO UNIVERSITÁRIO DE LISBOA ISCTE IUL   (Portugal)

Author keywords

Behavioural diversity; Deception; Evolutionary robotics; NEAT; Neuroevolution; Novelty search; Swarm robotics

Indexed keywords

HEALTH; ROBOTICS;

BEHAVIOURAL DIVERSITY; DECEPTION; EVOLUTIONARY ROBOTICS; NEAT; NEURO EVOLUTIONS; NOVELTY SEARCH; SWARM ROBOTICS;

SWARM INTELLIGENCE;

EID: 84880792945     PISSN: 19353812     EISSN: 19353820     Source Type: Journal    
DOI: 10.1007/s11721-013-0081-z     Document Type: Article

References (63)

1. Ampatzis, C., Tuci, E., Trianni, V., & Dorigo, M. (2008). Evolution of signaling in a multi-robot system: categorization and communication. Adaptive Behavior, 16(1), 5-26.
2. Bahgeçi, E., & Şahin, E. (2005). Evolving aggregation behaviors for swarm robotic systems: a systematic case study. In Swarm intelligence symposium (pp. 333-340). New York: IEEE Press.
3. Baldassarre, G., Nolfi, S., & Parisi, D. (2003). Evolving mobile robots able to display collective behaviors. Artificial Life, 9(3), 255-268.
4. Baldassarre, G., Trianni, V., Bonani, M., Mondada, F., Dorigo, M., & Nolfi, S. (2007). Self-organized coordinated motion in groups of physically connected robots. IEEE Transactions on Systems, Man and Cybernetics, 37(1), 224-239.
5. Bayindir, L., & Şahin, E. (2007). A review of studies in swarm robotics. Turkish Journal of Electrical Engineering and Computer Sciences, 15(2), 115-147.
6. Bonabeau, E., Dorigo, M., & Theraulaz, G. (1999). Swarm intelligence: from natural to artificial systems. New York: Oxford University Press.
7. Brambilla, M., Ferrante, E., Birattari, M., & Dorigo, M. (2013). Swarm robotics: a review from the swarm engineering perspective. Swarm Intelligence, 7(1), 1-41.
8. Cao, Y. U., Fukunaga, A. S., & Kahng, A. B. (1997). Cooperative mobile robotics: antecedents and directions. Autonomous Robots, 4(1), 7-27.
9. Castelli, M., Manzoni, L., & Vanneschi, L. (2011). A method to reuse old populations in genetic algorithms. In LNCS: Vol. 7026. Portuguese conference on artificial intelligence (EPIA) (pp. 138-152). Berlin: Springer.
10. Chellapilla, K., & Fogel, D. B. (1999). Evolving neural networks to play checkers without relying on expert knowledge. IEEE Transactions on Neural Networks, 10(6), 1382-1391.
11. Correll, N., & Martinoli, A. (2007). Modeling self-organized aggregation in a swarm of miniature robots. In IEEE international conference on robotics and automation (ICRA) (pp. 379-384). New York: IEEE Press.
12. Cuccu, G., & Gomez, F. J. (2011). When novelty is not enough. In LNCS: Vol. 6624. European conference on the applications of evolutionary computation (EvoApplications) (pp. 234-243). Berlin: Springer.
13. Cuccu, G., Gomez, F. J., & Glasmachers, T. (2011). Novelty-based restarts for evolution strategies. In IEEE congress on evolutionary computation (IEEE CEC) (pp. 158-163). New York: IEEE Press.
14. Deb, K. (2001). Multi-objective optimization using evolutionary algorithms. Hoboken: Wiley.
15. Doncieux, S., & Mouret, J.-B. (2010). Behavioral diversity measures for evolutionary robotics. In IEEE congress on evolutionary computation (IEEE CEC) (pp. 1-8). New York: IEEE Press.
16. Doncieux, S., Mouret, J.-B., Bredeche, N., & Padois, V. (2011). Evolutionary robotics: exploring new horizons. In Studies in computational intelligence: Vol. 341. New horizons in evolutionary robotics (pp. 3-25). Berlin: Springer.
17. Goldberg, D. E., & Richardson, J. (1987). Genetic algorithms with sharing for multimodal function optimization. In Genetic algorithms and their applications: second international conference on genetic algorithms (pp. 41-49). Mahwah: Erlbaum.
18. Gomes, J., Urbano, P., & Christensen, A. L. (2012). Progressive minimal criteria novelty search. In LNAI: Vol. 7637. Ibero-American conference on artificial intelligence (IBERAMIA) (pp. 281-290). Berlin: Springer.
19. Gomez, F., & Mikkulainen, R. (1997). Incremental evolution of complex general behavior. Adaptative Behaviour, 5(3-4), 317-342.
20. Gross, R., & Dorigo, M. (2008). Evolution of solitary and group transport behaviors for autonomous robots capable of self-assembling. Adaptive Behavior, 16(5), 285-305.
21. Gutiérrez, A., Campo, A., Dorigo, M., Amor, D., Magdalena, L., & Monasterio-Huelin, F. (2008). An open localization and local communication embodied sensor. Sensors, 8(11), 7545-7563.
22. Harvey, I., Husbands, P., & Cliff, D. (1993). Issues in evolutionary robotics. In International conference on simulation of adaptive behavior (SAB) (pp. 364-373). Cambridge: MIT Press.
23. Hauert, S., Zufferey, J.-C., & Floreano, D. (2009). Evolved swarming without positioning information: an application in aerial communication relay. Autonomous Robots, 26(1), 21-32.
24. Hornby, G. (2006). ALPS: the age-layered population structure for reducing the problem of premature convergence. In Genetic and evolutionary computation conference (GECCO) (pp. 815-822). New York: ACM.
25. Hu, J., Goodman, E. D., Seo, K., Fan, Z., & Rosenberg, R. (2005). The hierarchical fair competition (HFC) framework for sustainable evolutionary algorithms. Evolutionary Computation, 13(2), 241-277.
26. Hugues, L., & Bredeche, N. (2006). Simbad: an autonomous robot simulation package for education and research. In LNCS: Vol. 4095. International conference on simulation of adaptive behavior (SAB) (pp. 831-842). Berlin: Springer.
27. Hutter, M., & Legg, S. (2006). Fitness uniform optimization. IEEE Transactions on Evolutionary Computation, 10(5), 568-589.
28. Jeanson, R., Rivault, C., Deneubourg, J.-L., Blanco, S., Fournier, R., Jost, C., & Theraulaz, G. (2005). Self-organized aggregation in cockroaches. Animal Behaviour, 69(1), 169-180.
29. Jones, T., & Forrest, S. (1995). Fitness distance correlation as a measure of problem difficulty for genetic algorithms. In International conference on genetic algorithms (ICGA) (pp. 184-192). San Mateo: Morgan Kaufmann.
30. Kernbach, S., & Kernbach, O. (2011). Collective energy homeostasis in a large-scale microrobotic swarm. Robotics and Autonomous Systems, 59(12), 1090-1101.
31. Kirkpatrick, D. A. (2012) Novelty search in competitive coevolution using normalized compression distance. Master thesis, College of Engineering, Florida Institute of Technology.
32. Kistemaker, S., & Whiteson, S. (2011). Critical factors in the performance of novelty search. In Genetic and evolutionary computation conference (GECCO) (pp. 965-972). New York: ACM.
33. Knowles, J., Watson, R., & Corne, D. (2001). Reducing local optima in single-objective problems by multi-objectivization. In LNCS: Vol. 1993. Evolutionary multi-criterion optimization (pp. 269-283). Berlin: Springer.
34. Kohonen, T. (1990). The self-organizing map. Proceedings of the IEEE, 78(9), 1464-1480.
35. Krcah, P. (2010). Solving deceptive tasks in robot body-brain co-evolution by searching for behavioral novelty. In International conference on intelligent systems design and applications (ISDA) (pp. 284-289). New York: IEEE Press.
36. Lehman, J., & Stanley, K. O. (2008). Exploiting open-endedness to solve problems through the search for novelty. In International conference on the synthesis and simulation of living systems (ALIFE) (pp. 329-336). Cambridge: MIT Press.
37. Lehman, J., & Stanley, K. O. (2010a). Revising the evolutionary computation abstraction: minimal criteria novelty search. In Genetic and evolutionary computation conference (GECCO) (pp. 103-110). New York: ACM.
38. Lehman, J., & Stanley, K. O. (2010b). Efficiently evolving programs through the search for novelty. In Genetic and evolutionary computation conference (GECCO) (pp. 837-844). New York: ACM.
39. Lehman, J., & Stanley, K. O. (2011a). Abandoning objectives: evolution through the search for novelty alone. Evolutionary Computation, 19(2), 189-223.
40. Lehman, J., & Stanley, K. O. (2011b). Evolving a diversity of virtual creatures through novelty search and local competition. In Genetic and evolutionary computation conference (GECCO) (pp. 211-218). New York: ACM.
41. Liu, W., Winfield, A. F. T., & Sa, J. (2007). Modelling swarm robotic systems: a case study in collective foraging. In Towards autonomous robotic systems (TAROS) (pp. 25-32).
42. Michaud, F., & Robichaud, E. (2002). Sharing charging stations for long-term activity of autonomous robots. In IEEE/RSJ international conference on intelligent robots and systems (IROS) (Vol. 3, pp. 2746-2751). New York: IEEE Press.
43. Mondada, F., Bonani, M., Raemy, X., Pugh, J., Cianci, C., Klaptocz, A., Magnenat, S., Zufferey, J.-C., Floreano, D., & Martinoli, A. (2009). The e-puck, a robot designed for education in engineering. In 9th conference on autonomous robot systems and competitions (ROBOTICA) (pp. 59-65). Castelo Branco: IPCB.
44. Mouret, J.-B. (2011). Novelty-based multiobjectivization. In Studies in computational intelligence: Vol. 341. New horizons in evolutionary robotics (pp. 139-154). Berlin: Springer.
45. Mouret, J.-B., & Doncieux, S. (2009). Overcoming the bootstrap problem in evolutionary robotics using behavioral diversity. In IEEE congress on evolutionary computation (IEEE CEC) (pp. 1161-1168). New York: IEEE Press.
46. Mouret, J.-B., & Doncieux, S. (2012). Encouraging behavioral diversity in evolutionary robotics: an empirical study. Evolutionary Computation, 20(1), 91-133.
47. Muñoz Meléndez, A., Sempé, F., & Drogoul, A. (2002). Sharing a charging station without explicit communication in collective robotics. In International conference on simulation of adaptive behavior (SAB) (pp. 383-384). Cambridge: MIT Press.
48. Nelson, A. L., Barlow, G. J., & Doitsidis, L. (2009). Fitness functions in evolutionary robotics: a survey and analysis. Robotics and Autonomous Systems, 57(4), 345-370.
49. Pini, G., & Tuci, E. (2008). On the design of neuro-controllers for individual and social learning behaviour in autonomous robots: an evolutionary approach. Connection Science, 20(2-3), 211-230.
50. Şahin, E. (2005). Swarm robotics: from sources of inspiration to domains of application. In LNCS: Vol. 3342. International workshop on swarm robotics (pp. 10-20). Berlin: Springer.
51. Schmidt, M., & Lipson, H. (2011). Age-fitness Pareto optimization. In Genetic and evolutionary computation: Vol. 8. Genetic programming theory and practice VIII (pp. 129-146). Berlin: Springer.
52. Soltoggio, A., & Jones, B. (2009). Novelty of behaviour as a basis for the neuro-evolution of operant reward learning. In Genetic and evolutionary computation conference (GECCO) (pp. 169-176). New York: ACM.
53. Soysal, O., Bahgeçi, E., & Şahin, E. (2007). Aggregation in swarm robotic systems: evolution and probabilistic control. Turkish Journal of Electrical Engineering and Computer Sciences, 15(2), 199-225.
54. Sperati, V., Trianni, V., & Nolfi, S. (2008). Evolving coordinated group behaviours through maximisation of mean mutual information. Swarm Intelligence, 2(2-4), 73-95.
55. Sprong, C. (2011). Common tasks in evolutionary robotics, an overview. Technical report, Faculty of Sciences, University of Amsterdam, Netherlands. URL http://www. few. vu. nl/nl/Images/werkstuk-sprong_tcm38-217791. pdf.
56. Stanley, K. O., & Miikkulainen, R. (2002). Evolving neural network through augmenting topologies. Evolutionary Computation, 10(2), 99-127.
57. Trianni, V. (2008). Studies in computational intelligence: Vol. 108. Evolutionary swarm robotics: evolving self-organising behaviours in groups of autonomous robots. Berlin: Springer.
58. Trianni, V., Gross, R., Labella, T. H., Şahin, E., & Dorigo, M. (2003). Evolving aggregation behaviors in a swarm of robots. In LNCS: Vol. 2801. European conference on artificial life (ECAL) (pp. 865-874). Berlin: Springer.
59. Trianni, V., Nolfi, S., & Dorigo, M. (2006). Cooperative hole avoidance in a swarm-bot. Robotics and Autonomous Systems, 54(2), 97-103.
60. Uchibe, E., Yanase, M., & Asada, M. (2002). Behavior generation for a mobile robot based on the adaptive fitness function. Robotics and Autonomous Systems, 40(2-3), 69-77.
61. Watson, R. A., & Pollack, J. B. (2001). Coevolutionary dynamics in a minimal substrate. In Genetic and evolutionary computation conference (GECCO) (pp. 702-709). San Mateo: Morgan Kaufmann.
62. Whitley, L. D. (1991). Fundamental principles of deception in genetic search. In Foundations of genetic algorithms (pp. 221-241). San Mateo: Morgan Kaufmann.
63. Zaera, N., Cliff, D., & Bruten, J. (1996). (Not) Evolving collective behaviours in synthetic fish. In International conference on simulation of adaptive behavior (SAB) (pp. 635-644). Cambridge: MIT Press.