Abstract
The parallelization of evolutionary computation tasks using a coarse-grained approach can be efficiently achieved using the island migration model. Strongly influenced by the theory of punctuated equilibria, such a scheme guarantees an efficient exchange of genetic material between niches, not only accelerating but also improving the evolutionary process. We study the island model computational paradigm in relation to the evolutionary robotics methodology. We let populations of robots evolve in different islands of an archipelago and exchange individuals along allowed migration paths. We show, for the test-case selected, how the exchange of genetic material coming from different islands improves the overall design efficiency and speed, effectively taking advantage of a parallel computing environment to improve the methodology of evolutionary robotics, often criticized for its computational cost.
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References
Ampatzis, C., Tuci, E., Trianni, V., Christensen, A.L., Dorigo, M.: Evolving self-assembly in autonomous homogeneous robots: experiments with two physical robots. Artificial Life 15(4), 1–20 (2009)
Beer, R.D., Gallagher, J.C.: Evolving dynamical neural networks for adaptive behavior. Adaptive Behavior 1, 91–122 (1992)
Cantu-Paz, E.: Efficient and Accurate Parallel Genetic Algorithms. Kluwer Academic Publishers, Norwell (2000)
Cohoon, J., Hegde, S., Martin, W., Richards, D.: Punctuated equilibria: a parallel genetic algorithm. In: Proceedings of the Second International Conference on Genetic Algorithms and Their Application, pp. 148–154. L. Erlbaum Associates Inc., Hillsdale (1987)
Izzo, D., Ruciński, M., Ampatzis, C.: Parallel global optimisation meta-heuristics using an asynchronous island-model. In: Proceedings of CEC 2009 (2009)
Kleywegt, A.J., Shapiro, A., Homem de Mello, T.: The sample average approximation method for stochastic discrete optimization. SIAM Journal on Optimization 12(2), 479–502 (2002)
Martin, W.N., Lienig, J., Cohoon, J.P.: Island (migration) models: evolutionary algorithms based on punctuated equilibria, ch. C6.3:1-C6.3:16. Institute of Physics Publishing, Bristol (1997)
Mataric, M., Cliff, D.: Challenges in evolving controllers for physical robots. Robotics and Autonomous Systems 19(1), 67–83 (1996)
Eldredge, N., Gould, S.J.: Punctuated Equilibria: An Alternative to Phyletic Gradualism. Freeman, Cooper and Co., New York (1972)
Nolfi, S., Floreano, D.: Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines. MIT Press, Cambridge (2000)
Storn, R., Price, K.: Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11(4), 341–359 (1997)
Tasoulis, D., Pavlidis, N., Plagianakos, V., Vrahatis, M.: Parallel differential evolution. In: IEEE Congress on Evolutionary Computation (2004)
Tuci, E., Ampatzis, C., Christensen, A.L., Trianni, V., Dorigo, M.: Self-assembly in physical autonomous robots: the evolutionary robotics approach. In: Proceedings of ALIFE XI, pp. 616–623. MIT Press, Cambridge (2008)
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Ampatzis, C., Izzo, D., Ruciński, M., Biscani, F. (2011). ALife in the Galapagos: Migration Effects on Neuro-Controller Design. In: Kampis, G., Karsai, I., Szathmáry, E. (eds) Advances in Artificial Life. Darwin Meets von Neumann. ECAL 2009. Lecture Notes in Computer Science(), vol 5777. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21283-3_25
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DOI: https://doi.org/10.1007/978-3-642-21283-3_25
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