  We have conceived and implemented a multi-objective genetic algorithm (GA)
code for the optimisation of an array of Imaging Atmospheric Cherenkov
Telescopes (IACTs). The algorithm takes as input a series of cost functions
(metrics) each describing a different objetive of the optimisation (such as
effective area, angular resolution, etc.), all of which are expressed in terms
of the relative position of the telescopes in the plane. The output of the
algorithm is a family of geometrical arrangements which correspond to the
complete set of solutions to the array optimisation problem, and differ from
each other according to the relative weight given to each of the (maybe
conflicting) objetives of the optimisation. Since the algorithm works with
parallel optimisation it admits as many cost functions as desired, and can
incorporate constraints such as budget (cost cap) for the array and topological
limitations of the terrain, like geographical accidents where telescopes cannot
be installed. It also admits different types of telescopes (hybrid arrays) and
the number of telescopes of each type can be treated as a parameter to be
optimised - constrained, for example, by the cost of each type or the energy
range of interest. The purpose of the algorithm, which converges fast to
optimised solutions (if compared to the time for a complete Monte Carlo
Simulation of a single configuration), is to provide a tool to investigate the
full parameter space of possible geometries, and help in designing complex
arrays. It does not substitute a detailed Monte Carlo study, but aims to guide
it. In the examples of arrays shown here we have used as metrics simple
heuristic expressions describing the fundamentals of the IAC technique, but
these input functions can be made as detailed or complex as desired for a given
experiment.
