  Optimization algorithms are very different from human optimizers. A human
being would gain more experiences through problem-solving, which helps her/him
in solving a new unseen problem. Yet an optimization algorithm never gains any
experiences by solving more problems. In recent years, efforts have been made
towards endowing optimization algorithms with some abilities of experience
learning, which is regarded as experience-based optimization. In this paper, we
argue that hard optimization problems could be tackled efficiently by making
better use of experiences gained in related problems. We demonstrate our ideas
in the context of expensive optimization, where we aim to find a near-optimal
solution to an expensive optimization problem with as few fitness evaluations
as possible. To achieve this, we propose an experience-based surrogate-assisted
evolutionary algorithm (SAEA) framework to enhance the optimization efficiency
of expensive problems, where experiences are gained across related expensive
tasks via a novel meta-learning method. These experiences serve as the
task-independent parameters of a deep kernel learning surrogate, then the
solutions sampled from the target task are used to adapt task-specific
parameters for the surrogate. With the help of experience learning, competitive
regression-based surrogates can be initialized using only 1$d$ solutions from
the target task ($d$ is the dimension of the decision space). Our experimental
results on expensive multi-objective and constrained optimization problems
demonstrate that experiences gained from related tasks are beneficial for the
saving of evaluation budgets on the target problem.
