Abstract
Weighted intermediate recombination has been proven very useful in evolution strategies. We propose here to use it in the case of on-line embodied evolutionary algorithms. With this recombination scheme, solutions at the local populations are recombined using a weighted average that favors fitter solutions to produce a new solution. We describe the newly proposed algorithm which we dubbed (\(\mu /\mu _{\mathrm {W}},1\))-On-line EEA, and assess it performance on two swarm robotics benchmarks while comparing the results to other existing algorithms. The experiments show that the recombination scheme is very beneficial on these problems.
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Notes
- 1.
The authors use a virtual energy level in place of fitness.
- 2.
The term “same” is here used in the sense “originating from the same agent”.
References
Bredeche, N., Haasdijk, E., Prieto, A.: Embodied evolution in collective robotics: a review. Frontiers Robot. AI 5, 12 (2018)
Watson, R., Ficici, S., Pollack, J.: Embodied evolution: distributing an evolutionary algorithm in a population of robots. Robot. Auton. Syst. 39, 1–18 (2002)
Beyer, H.G.: Toward a theory of evolution strategies: on the benefits of sex - the \((\mu /\mu,\lambda )\) theory. Evol. Comput. 3(1), 81–111 (1995)
Arnold, D.V.: Noisy Optimization with Evolution Strategies. Springer, New York (2002). https://doi.org/10.1007/978-1-4615-1105-2
Karafotias, G., Haasdijk, E., Eiben, A.E.: An algorithm for distributed on-line, on-board evolutionary robotics. In: Proceedings of GECCO 2011, pp. 171–178. ACM (2011)
Schaffer, D.J., Whitley, D., Eshelman, L.J.: Combinations of genetic algorithms and neural networks: a survey of the state of the art. In: Proceedings of COGANN 1992, pp. 1–37 (1992)
Stanley, K.O., Miikkulainen, R.: Evolving neural networks through augmenting topologies. Evol. Comput. 10(2), 99–127 (2002)
Silva, F., Urbano, P., Oliveira, S., Christensen, A.L.: odNEAT: an algorithm for distributed online, onboard evolution of robot behaviours. In: Artificial Life. vol. 13, pp. 251–258. MIT Press (2012)
Fernández Pèrez, I.n., Boumaza, A., Charpillet, F.: Decentralized innovation marking for neural controllers in embodied evolution. In: Proceedings of GECCO 2015, pp. 161–168. ACM, Madrid (2015)
Bredeche, N., Montanier, J.-M.: Environment-driven embodied evolution in a population of autonomous agents. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds.) PPSN 2010. LNCS, vol. 6239, pp. 290–299. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15871-1_30
Hansen, N., Ostermeier, A.: Completely derandomized self-adaptation in evolution strategies. Evol. Comput. 9(2), 159–195 (2001)
Bredeche, N., Montanier, J.M., Weel, B., Haasdijk, E.: Roborobo! A fast robot simulator for swarm and collective robotics. CoRR abs/1304.2888 (2013)
Fernández Pèrez, I.n., Boumaza, A., Charpillet, F.: Comparison of selection methods in on-line distributed evolutionary robotics. In: Proceedings of ALIFE 2014, pp. 282–289. MIT Press, New York (2014)
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Boumaza, A. (2019). Introducing Weighted Intermediate Recombination in On-Line Collective Robotics, the (\(\mu /\mu _{\mathrm {W}},1\))-On-line EEA. In: Kaufmann, P., Castillo, P. (eds) Applications of Evolutionary Computation. EvoApplications 2019. Lecture Notes in Computer Science(), vol 11454. Springer, Cham. https://doi.org/10.1007/978-3-030-16692-2_42
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