Name
Abstract | ||
|---|---|---|
It had been discovered that not long jumps but short jumps with large variances among Cauchy mutations had contributed to the better performance of fast evolutionary programming (FEP) than that of classical evolutionary programming (CEP). One strong force to push the effective Cauchy mutations to have the shorter step sizes came from the different behaviors of the the same self-adaptation used in CEP and FEP on optimizing the same test functions from the same initial populations. This paper explored such correlation between the mutation step sizes and self-adaptation, and suggests that it is as necessary to set up an upper bound as to have a lower bound on the strategy parameters in self-adaptation. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/CEC.2010.5585944 | 2010 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC) |
Keywords | Field | DocType |
next generation networking,optimization,correlation,upper bound,benchmark testing,lower bound,evolutionary programming,evolutionary computation,programming | Mathematical optimization,Upper and lower bounds,Evolutionary computation,Cauchy distribution,Self adaptation,Artificial intelligence,Evolutionary programming,Machine learning,Benchmark (computing),Mathematics,Strong interaction | Conference |
Citations | PageRank | References |
0 | 0.34 | 10 |
Authors | ||
1 | ||