Name
Title | ||
|---|---|---|
Self-adaptive lower confidence bound: A new general and effective prescreening method for Gaussian Process surrogate model assisted evolutionary algorithms |
Abstract | ||
|---|---|---|
Surrogate model assisted evolutionary algorithms are receiving much attention for the solution of optimization problems with computationally expensive function evaluations. For small scale problems, the use of a Gaussian Process surrogate model and prescreening methods has proven to be effective. However, each commonly used prescreening method is only suitable for some types of problems, and the proper prescreening method for an unknown problem cannot be stated beforehand. In this paper, the four existing prescreening methods are analyzed and a new method, called self-adaptive lower confidence bound (ALCB), is proposed. The extent of rewarding the prediction uncertainty is adjusted on line based on the density of samples in a local area and the function properties. The exploration and exploitation ability of prescreening can thus be better balanced. Experimental results on benchmark problems show that ALCB has two main advantages: (1) it is more general for different problem landscapes than any of the four existing prescreening methods; (2) it typically can achieve the best result among all available prescreening methods. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/CEC.2012.6256585 | IEEE Congress on Evolutionary Computation |
Keywords | Field | DocType |
computational expensive function evaluations,optimisation,evolutionary computation,gaussian process surrogate model assisted evolutionary algorithms,self-adaptive lower confidence bound,prediction uncertainty,prescreening method,optimization problems,gaussian processes,alcb,vectors,uncertainty,predictive models,optimization,computational modeling,databases | Mathematical optimization,Evolutionary algorithm,Computer science,Evolutionary computation,Surrogate model,Self adaptive,Gaussian process,Artificial intelligence,Optimization problem,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4673-1508-1 | 2 | 0.44 |
References | Authors | |
4 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bo Liu | 1 | 38 | 4.72 |
| Qingfu Zhang | 2 | 7634 | 255.05 |
| Francisco V. Fernández | 3 | 234 | 40.82 |
| Georges G. E. Gielen | 4 | 2036 | 254.40 |