On strong homogeneity of two global optimization algorithms based on statistical models of multimodal objective functions
arXiv:1108.104258 citationsh-index: 26
Originality Synthesis-oriented
AI Analysis
Theoretical insight for algorithm designers working on global optimization with statistical models.
The paper introduces the property of strong homogeneity for global optimization algorithms and proposes a simple implementation using infinity arithmetic. It proves that the P-algorithm and one-step Bayesian algorithm are strongly homogeneous.
The implementation of global optimization algorithms, using the arithmetic of infinity, is considered. A relatively simple version of implementation is proposed for the algorithms that possess the introduced property of strong homogeneity. It is shown that the P-algorithm and the one-step Bayesian algorithm are strongly homogeneous.