Dynamic memory model for non-stationary optimization
C.N. Bendtsen, Thiemo Krink · 2003
Real-world problems are often nonstationary and can cause cyclic, repetitive patterns in the search landscape. For this class of problems, we introduce a new GA with dynamic explicit memory, which showed superior performance compared to a classic GA and a previously introduced memory-based GA for two dynamic benchmark problems.