A natural evolution strategy for multi-objective optimization

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6238 LNCS, Issue PART 1, 2010, Pages 627-636

  Glasmachers, Tobias ^a   Schaul, Tom ^a   Schmidhuber, Jürgen ^a  

^a UNIVERSITY OF LUGANO   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARK FUNCTIONS; CORE COMPONENTS; COVARIANCE MATRIX ADAPTATION EVOLUTION STRATEGIES; DESCENT METHOD; MULTI OBJECTIVE; NATURAL EVOLUTION; NATURAL GRADIENT; SINGLE OBJECTIVE OPTIMIZATION;

BIOLOGY; COVARIANCE MATRIX; EVOLUTIONARY ALGORITHMS; PROBLEM SOLVING;

MULTIOBJECTIVE OPTIMIZATION;

EID: 78149256553     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-15844-5_63     Document Type: Conference Paper

References (13)

1. Coello Coello, A.C., Lamont, G.B., Van Veldhuizen, D.A.: Evolutionary algorithms for solving multi-objective problems. Springer, New York (2007)
2. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182-197 (2002)
3. Glasmachers, T., Schaul, T., Sun, Y., Wierstra, D., Schmidhuber, J.: Exponential Natural Evolution Strategies. In: Genetic and Evolutionary Computation Conference, GECCO (2010)
4. Hansen, N., Ostermeier, A.: Completely Derandomized Self-Adaptation in Evolution Strategies. Evolutionary Computation 9(2), 159-195 (2001)
5. Igel, C., Hansen, N., Roth, S.: Covariance Matrix Adaptation for Multi-objective Optimization. Evolutionary Computation 15(1), 1-28 (2007)
6. Kern, S., Müller, S.D., Hansen, N., Büche, D., Ocenasek, J., Koumoutsakos, P.: Learning probability distributions in continuous evolutionary algorithms - a comparative review. Natural Computing 3(1), 77-112 (2004) (Pubitemid 39127660)
7. Knowles, J., Watson, R., Corne, D.: EMO 2001. LNCS, vol. 1993. Springer, Heidelberg (2001)
8. Rückstieß, T., Sehnke, F., Schaul, T., Wierstra, D., Yi, S., Schmidhuber, J.: Exploring parameter space in reinforcement learning. Paladyn Journal of Behavioral Robotics 1(1), 14-24 (2010)
9. Sun, Y., Wierstra, D., Schaul, T., Schmidhuber, J.: Efficient Natural Evolution Strategies. In: Genetic and Evolutionary Computation Conference, GECCO (2009)
10. Sun, Y., Wierstra, D., Schaul, T., Schmidhuber, J.: Stochastic Search using the Natural Gradient. In: International Conference on Machine Learning, ICML (2009)
11. Wierstra, D., Schaul, T., Peters, J., Schmidhuber, J.: Natural Evolution Strategies. In: Proceedings of the Congress on Evolutionary Computation (CEC 2008, Hongkong. IEEE Press, Los Alamitos (2008)
12. Zitzler, E., Künzli, S.: Indicator-Based Selection in Multiobjective Search. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo- Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 832-842. Springer, Heidelberg (2004)
13. Zitzler, E., Thiele, L.: Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 292-301. Springer, Heidelberg (1998) (Pubitemid 128145960)