On a hill-climbing algorithm with adaptive step size: Towards a control parameter-less black-box optimisation algorithm

Abstract

Many scientific and engineering problems can be viewed as search or optimisation problems, where an optimum input parameter vector for a given system has to be found in order to maximise or to minimise the system response to that input vector. Often, auxiliary information about the system, like its transfer function and derivatives, etc., is not known and the measures might be incomplete and distorted by noise. This makes such problems difficult to be solved by traditional mathematical methods. Here, heuristic optimisation algorithms, like Genetic Algorithms (GA) [1] or Simulated Annealing (SA) [2], can offer a solution. But because of the lack of a standard methodology for matching a problem with a suitable algorithm, and for setting the control parameters for the algorithm, practitioners often seem not to consider heuristic optimisation.