Comparing the Performance of Learnable Evolution Model LEM and Pattern Search as a Function Optimizer

The underlying paper presents a comparison of the learnable evolution model LEM and Pattern Search PS techniques as a function optimizer. In contrast to conventional Darwinian type evolutionary computation algorithm that uses various forms of mutation and/or recombination operators, LEM uses machine learning to guide the process of generating new individuals. It employs the AQ learning to generate hypotheses discriminating between groups of high and low fitness individuals, and then uses these hypotheses to generate new individuals. On the other hand pattern search is a class of direct search for derivative-free optimization with accurately established global convergence properties. Pattern search makes no use of derivative information, which might be unavailable, too expensive, or misleading. This paper focuses on measuring the performance of LEM3 and pattern search from the point of view of execution time in experiments on optimizing the Rastrigin function with different number of variables.