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Surrogate assisted evolutionary

Surrogate Assisted Evolutionary Algorithm for Multi-Objective Optimization... [Pg.131]

In this chapter, a surrogate assisted evolutionary algorithm (SAEA) that eliminates some of the problems discussed above is proposed. Its performance on a number of mathematical benchmarks is reported and compared with the results of NSGA-II. The features of the algorithm are discussed in Sec. 5.2 and the results are presented in Sec. 5.3. Summarized in Sec. 5.4 are the findings and some of the ongoing developments. [Pg.134]

The proposed Surrogate Assisted Evolutionary Algorithm is outlined in Algorithm 5.1. The MATLAB code of the algorithm is available in the folder Chapter 5 on the CD. [Pg.134]

Algorithm 5.1 Surrogate Assisted Evolutionary Algorithm Require No > 1 Number of Generations ... [Pg.135]

Ray, T. and Smith, W. (2006). Surrogate assisted evolutionary algorithm for multiobjective optimization, fith AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, pp. 1-8. [Pg.149]

Won, K. S. and Ray, T. (2004). Performance of kriging and cokriging based surrogate models within the unified framework for surrogate assisted optimization, in Proceedings of the IEEE Congress on Evolutionary Computation, CEC 04 (Portland). [Pg.149]

See other pages where Surrogate assisted evolutionary is mentioned: [Pg.20]    [Pg.21]    [Pg.131]    [Pg.133]    [Pg.134]    [Pg.135]    [Pg.137]    [Pg.139]    [Pg.141]    [Pg.143]    [Pg.145]    [Pg.147]    [Pg.147]    [Pg.149]    [Pg.151]    [Pg.133]    [Pg.20]    [Pg.21]    [Pg.131]    [Pg.133]    [Pg.134]    [Pg.135]    [Pg.137]    [Pg.139]    [Pg.141]    [Pg.143]    [Pg.145]    [Pg.147]    [Pg.147]    [Pg.149]    [Pg.151]    [Pg.133]    [Pg.133]    [Pg.442]    [Pg.49]   


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