  Bayesian optimisation is a well-known sample-efficient method for the
optimisation of expensive black-box functions. However when dealing with big
search spaces the algorithm goes through several low function value regions
before reaching the optimum of the function. Since the function evaluations are
expensive in terms of both money and time, it may be desirable to alleviate
this problem. One approach to subside this cold start phase is to use prior
knowledge that can accelerate the optimisation. In its standard form, Bayesian
optimisation assumes the likelihood of any point in the search space being the
optimum is equal. Therefore any prior knowledge that can provide information
about the optimum of the function would elevate the optimisation performance.
In this paper, we represent the prior knowledge about the function optimum
through a prior distribution. The prior distribution is then used to warp the
search space in such a way that space gets expanded around the high probability
region of function optimum and shrinks around low probability region of
optimum. We incorporate this prior directly in function model (Gaussian
process), by redefining the kernel matrix, which allows this method to work
with any acquisition function, i.e. acquisition agnostic approach. We show the
superiority of our method over standard Bayesian optimisation method through
optimisation of several benchmark functions and hyperparameter tuning of two
algorithms: Support Vector Machine (SVM) and Random forest.
