Effective matrix adaptation strategy for noisy derivative-free optimization
- In this paper, we introduce a new effective matrix adaptation evolution strategy
(MADFO) for noisy derivative-free optimization problems. Like every MAES solver,
MADFO consists of three phases: mutation, selection and recombination. MADFO
improves the mutation phase by generating good step sizes, neither too small not
too large, that increase the probability of selecting mutation points with small inexact
function values in the selection phase. In the recombination phase, a recombination
point with lowest inexact function value found among all evaluated points so far may
be found by a new randomized non-monotone line search method and accepted as
the best point. If no best point is found, a heuristic point may be accepted as the best
point. We compare MADFO with state-of-the-art DFO solvers on noisy test problems
obtained by adding various kinds and levels of noise to all unconstrained CUTEst
test problems with dimensions n ≤ 20, and find that MADFO has the highest number
of solved problems
