On evolutionary optimization of large problems using small populations

Lecture Notes in Computer Science

Volumn 3611, Issue PART II, 2005, Pages 1145-1154

  Jin, Yaochu ^a   Olhofer, Markus ^a   Sendhoff, Bernhard ^a  

^a HONDA RESEARCH INSTITUTE EUROPE GMBH   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATION THEORY; EVOLUTIONARY ALGORITHMS; OPTIMIZATION;

COMPUTATIONAL RESOURCES; EVOLUTIONARY OPTIMIZATIONS; SEARCH DIMENSIONS;

COMPUTER SCIENCE;

EID: 26844569483     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/11539117_154     Document Type: Conference Paper

References (7)

1. Jin, Y., Olhofer, M., Sendhoff, B.: A framework for evolutionary optimization with approximate fitness functions. IEEE Transactions on Evolutionary Computation. 6 (2002) 481-494
2. Hansen N., Ostermeier A.: Completely derandomized self-adaptation in evolution strategies, Evolutionary Computation 9 (2001) 159-195
3. Hansen, N., Kern S.: Evaluating the CMA evolution strategy on multimodal test functions. Parallel Problem Solving from Nature, Vol. 3242. Springer (2004) 282-291
4. Olhofer, M., Jin, Y., Sendhoff, B.: Adaptive encoding for aerodynamic shape optimization using evolution strategies. Congress on Evolutionary Computation (2001) 576-583
5. Hansen, N., Ostermeier, A.: Adapting arbitrary normal mutation distributions in evolution strategies: The covariance Matrix Adaptation. IEEE Conf. on Evolutionary Computation (1996) 312-317
6. Hansen, N., Müller, S., Koumoutsakos, P.: Reducing the time complexity of the derandomized evolution startegy with covariance matrix adaptation (CMA-ES). Evolutionary Computation. 11 (2003) 1-18
7. Jin, Y. (ed.): Knowledge Incorporation in Evolutionary Computation. Springer, Berlin Heidelberg (2005)