*** SUBMISSIONS DUE 15 25 JUNE 2016 ***
CBBOC is designed to provide the GECCO community with detailed performance comparisons of a wide variety of meta-heuristics and hyper-heuristics on combinatorial problems, where the real-world problems which induce combinatorial problems have been categorized into those with no training time (good fit for parameter-less algorithms), those with short training time (good fit for typical evolutionary algorithms), and those with long training time (good fit for hyper-heuristics). Training and testing time is measured in terms of number of fitness evaluations, although wall time will be used to time-out algorithms taking infeasibly long to complete. Competitors choose which category or categories they want to submit to. While trained differently, all three categories will be compared employing instances drawn from the same test set. This can create a Pareto set of winners, maximizing solution quality while minimizing training time, with at most three nondominated points. The competition problems will be randomly generated by a meta-class based on Mk-Landscapes which can represent all NK-Landscapes, Ising Spin Glasses, MAX-kSAT, Concatenated Traps, etc. (this is a generalization of the NK-Landscapes meta-class employed in the GECCO 2015 CBBOC). Note that you do not have to attend GECCO 2016 to participate, although all contestants are encouraged to attend the awards ceremony at GECCO 2016. For more detailed information, including API download links for C++, C#, Java, and Python, see the CBBOC website (http://web.mst.edu/~tauritzd/CBBOC/GECCO2016).
Alex Bertels, Missouri University of Science and Technology
Brian Goldman, Colorado State University
Jerry Swan, University of York
Daniel Tauritz, Missouri University of Science and Technology