Genetic Algorithms are very powerful search methods that are used in different optimization problems. Parallel versions of genetic algorithms are easily implemented and usually in...
— We consider an end-to-end approach of inferring network faults that manifest in multiple protocol layers, with an optimization goal of minimizing the expected cost of correctin...
In this paper, we describe how a genetic algorithm approach added to a simulated annealing (SA) process offers a better alternative to find the mean variance frontier in the portf...
Miguel A. Gomez, Carmen X. Flores, Maria A. Osorio
In an assembly line with high labor proportion, the workforce planning and scheduling is a very complex problem. At the background of increasing labor costs, it is very important ...
Robust optimization has traditionally focused on uncertainty in data and costs in optimization problems to formulate models whose solutions will be optimal in the worstcase among ...
Kedar Dhamdhere, Vineet Goyal, R. Ravi, Mohit Sing...