Category Archives: Conferences

GECCO 2016: Preliminary version of detailed program available

The preliminary version of the GECCO 2016 detailed program is available. In addition, the
GECCO 2016 Industrial Applications & EC in Practice Day program us available, too.
The GECCO 2016 Industrial Applications & EC in Practice Day is dedicated to the discussion of issues related to practical applications of Evolutionary Computation. During one day (Wednesday July 20, 2016), the most relevant topics related to real-world applications are discussed from various perspectives. It is a great forum for meeting the leading experts in this important field. This day starts with the “Industrial Applications of Metaheuristics” workshop, followed by two “Evolutionary Computation in Practice” sessions. A panel discussion with experts concludes this day. More: http://www.spotseven.de/program-available-industrial-applications-evolutionary-computation-in-practice-day-at-gecco-2016/

 

Program Available: Industrial Applications & Evolutionary Computation in Practice Day at GECCO 2016

The GECCO 2016 Industrial Applications & EC in Practice Day is dedicated to the discussion of issues related to practical applications of Evolutionary Computation. During one day (Wednesday July 20, 2016), the most relevant topics related to real-world applications are discussed from various perspectives.
It is a great forum for meeting the leading experts in this important field.
This day starts with the “Industrial Applications of Metaheuristics” workshop, followed by two “Evolutionary Computation in Practice” sessions. A panel discussion with experts concludes this day. Continue reading

2nd Combinatorial Black-Box Optimization Competition (CBBOC)

July 20-24, 2016 @ GECCO 2016 in Denver, Colorado, U.S.A.

http://web.mst.edu/~tauritzd/CBBOC/GECCO2016/

*** 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). Continue reading