How to use the AMD Threadripper for Scientific Computations

In GCMA-ES we introduced a new optimization algorithm and applied it to different real world problems Around the World in Eighty Days and GTOC1 today . Not only are these problems related to a real world tasks - space flight planning - we validated the optimization results by using them as basis for further optimization using a more detailed model of reality: continous low thrust. Using a genetic crossover operation in the context of a CMA-ES retry mechanism feels quite natural so we are probably not the first ones exploring this idea. But the devil is in the details: How to define the crossover operation  How to select ancestors for crossover  How to parameterize the CMA-ES runs  Here we will present more details together with some thoughts about metaparameter optimization. We are aware that the notion G-CMA-ES exists in some early CMA-ES papers in a different context, but we think nevertheless Genetic CMA-ES (GCMA-ES) is a good name for this retry approach...

In this blog entry we review methods described in (1) "1st ACT global trajectory  optimization  competition: Results found at  the Jet Propulsion Laboratory" (Acta Astronautica 61 (2007) 806–815)"  to solve the GTOC 1 problem back in 2006. Different aspects of the methods used back then are compared to what we regard more suitable to the set of tools available today.  Analysis of the JPL method How did JPL determine the planet sequence ? JPL used forward search using a sims flanagan approximation of the required  continuous  thrust transfers between the GA  maneuvers . Forward search is motivated by the fact that there are only very few valid transfers with v_inf <= 2.5 km/s because of the very low max_thrust constraint of  0.04N.  Problem is to find a search heuristics to be used for pruning targeting at a high impact velocity at the asteroid. Searching backwards from the impact can be motivated in a similar way: Very few valid...