Strategies for Evolving Diverse and Effective Behaviours in Pursuit Domains
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-56855-8_21
Reference21 articles.
1. Alhejali, A.M., Lucas, S.M.: Evolving diverse ms. pac-man playing agents using genetic programming. In: 2010 UK Workshop on Computational Intelligence (UKCI), pp. 1–6. IEEE (2010)
2. Bentley, P.J., Wakefield, J.P.: Finding acceptable solutions in the pareto-optimal range using multiobjective genetic algorithms. In: Chawdhry, P.K., Roy, R., Pant, R.K. (eds.) Soft Computing in Engineering Design and Manufacturing, pp. 231–240. Springer London, London (1998). https://doi.org/10.1007/978-1-4471-0427-8_25
3. Bullen, T., Katchabaw, M.: Using genetic algorithms to evolve character behaviours in modern video games. In: Proceedings of the GAMEON-NA (2008)
4. Chatzilygeroudis, K.I., Cully, A., Vassiliades, V., Mouret, J.: Quality-diversity optimization: a novel branch of stochastic optimization. CoRR (2020). https://arxiv.org/abs/2012.04322
5. Corne, D.W., Knowles, J.D.: Techniques for highly multiobjective optimisation: some nondominated points are better than others. In: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, pp. 773–780 (2007)
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