Most applications of evolutionary algorithms (EAs) deal with static optimization problems. However, in recent years, there has been a growing interest in timevarying (dynamic) prob...
Rasmus K. Ursem, Thiemo Krink, Mikkel T. Jensen, Z...
While performance specifications are verified before sign-off for a modern nanometer scale design, extensive application of optical proximity correction substantially alters the l...
Puneet Gupta, Andrew B. Kahng, Youngmin Kim, Denni...
This work provides an analysis of using the evolutionary algorithm EPNet to create ensembles of artificial neural networks to solve a range of forecasting tasks. Several previous...
Real life optimization problems often require finding optimal solution to complex high dimensional, multimodal problems involving computationally very expensive fitness function e...
It has long been known that a fixed ordering of optimization phases will not produce the best code for every application. One approach for addressing this phase ordering problem ...
Prasad Kulkarni, Stephen Hines, Jason Hiser, David...