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Real-time searches for faint radio pulses from unknown radio transients arecomputationally challenging. Detections become further complicated due tocontinuously increasing technical capabilities of transient surveys: telescopesensitivity, searched area of the sky, number of antennas or dishes, temporaland frequency resolution. The new Apertif transient survey on the Westerborktelescope happens in real-time on GPUs by means of the single-pulse searchpipeline AMBER (Sclocco, 2017). AMBER initially carries out auto tuning: itfinds the most optimal configuration of user-controlled parameters per each offour pipeline kernels so that each kernel performs its task as fast aspossible. The pipeline uses a brute-force (BF) exhaustive search which in totaltakes 5 - 24 hours to run depending on the processing cluster architecture. Weapply more heuristic, biologically driven genetic algorithms (GAs) to limit theexploration of the total parameter space, tune all four kernels together andreduce the tuning time to few hours. Our results show that after only few hoursof tuning, GAs always find similar or even better configurations for allkernels together than the combination of single kernel configurations tuned bythe BF approach. At the same time, by means of their genetic operators, GAsconverge into better solutions than those obtained by pure random searches. Theexplored multi-dimensional parameter space is very complex and has multiplelocal optima as the evolution of randomly generated configurations does notalways guarantee global solution.

The Apertif Monitor for Bursts Encountered in Real-time (AMBER) auto-tuning optimization with genetic algorithms