Stochastic optimization problems provide a means to model uncertainty in the input data where the uncertainty is modeled by a probability distribution over the possible realizatio...
Multi-objective optimization methods are essential to resolve real-world problems as most involve several types of objects. Several multi-objective genetic algorithms have been pro...
Mifa Kim, Tomoyuki Hiroyasu, Mitsunori Miki, Shiny...
Abstract. Many agent coordination problems can be modeled as distributed constraint optimization problems (DCOPs). ADOPT is an asynchronous and distributed search algorithm that is...
Many kernel learning algorithms, including support vector machines, result in a kernel machine, such as a kernel classifier, whose key component is a weight vector in a feature sp...
In this work, we consider a retailer selling a single product with limited on-hand inventory over a finite selling season. Customer demand arrives according to a Poisson process,...