Recent Research Topics in Evolutionary Multiobjective Optimization: A Personal Perspective

Author:

Coello Coello Carlos A.

Publisher

Springer International Publishing

Reference213 articles.

1. Abouhawwash, M., Deb, K.: Karush-Kuhn-Tucker proximity measure for multi-objective optimization based on numerical gradients. In: 2016 Genetic and Evolutionary Computation Conference (GECCO’2016), pp. 525–532. ACM Press, Denver, Colorado, USA (20–24 July 2016). ISBN: 978-1-4503-4206-3

2. Akhtar, T., Shoemaker, C.A.: Multi objective optimization of computationally expensive multi-modal functions with RBF surrogates and multi-rule selection. J. Global Optim. 64(1), 17–32 (2016)

3. Alves Ribeiro, V.H., Reynoso-Meza, G.: Multi-objective support vector machines ensemble generation for water quality monitoring. In: 2018 IEEE Congress on Evolutionary Computation (CEC’2018), pp. 608–613. IEEE Press, Rio de Janeiro, Brazil (8–13 July 2018). ISBN: 978-1-5090-6017-7

4. Aytug, H., Sayin, S.: using support vector machines to learn the efficient set in multiple objective discrete optimization. Eur. J. Oper. Res. 193(2), 510–519 (1 March 2009)

5. Azzouz, N., Bechikh, S., Said, L.B.: Steady state IBEA assisted by MLP neural networks for expensive multi-objective optimization problems. In: 2014 Genetic and Evolutionary Computation Conference (GECCO 2014), pp. 581–588. ACM Press, Vancouver, Canada (12–16 July 2014). ISBN: 978-1-4503-2662-9

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