Among the several non-conventional processes, electrical discharge machining (EDM) is the most widely and successfully applied for the machining of conductive parts. In this technique, the tool has no mechanical contact with the work piece and also the hardness of work piece has no effect on the machining pace. Hence, this technique could be employed to machine hard materials such as super allo...

Machine learning algorithms have been investigated in several scenarios, one of them is the data classification. The predictive performance of the models induced by these algorithms is usually strongly affected by the values used for their hyper-parameters. Different approaches to define these values have been proposed, like the use of default values and optimization techniques. Although defaul...

As compared to many other techniques used in natural language processing, hidden markov models (HMMs) are an extremely flexible tool and has been successfully applied to a wide variety of stochastic modeling tasks. This paper uses a machine learning approach to examine the effectiveness of HMMs on extracting information of varying levels of structure. A stochastic optimization procedure is used...

Assigning operations to feasible and suitable machine-tool combination is one of the significant problems in a flexible manufacturing system (FMS). This assignment affects on the total production costs and thus selecting the best combination is very significant for the system. This paper considers a multi-objective problem that minimizes the machining cost, setup cost and material handling cost...

MM (majorization–minimization) algorithms are an increasingly popular tool for solving optimization problems in machine learning and statistical estimation. This article introduces the MM algorithm framework in general and via three popular example applications: Gaussian mixture regressions, multinomial logistic regressions, and support vector machines. Specific algorithms for the three example...

The fields of machine learning and mathematical programming are increasingly intertwined. Optimization problems lie at the heart of most machine learning approaches. The Special Topic on Machine Learning and Large Scale Optimization examines this interplay. Machine learning researchers have embraced the advances in mathematical programming allowing new types of models to be pursued. The special...

this paper focuses on multi-objective designing of multi-machine thyristor controlled series compensator (tcsc) using strength pareto evolutionary algorithm (spea). the tcsc parameters designing problem is converted to an optimization problem with the multi-objective function including the desired damping factor and the desired damping ratio of the power system modes, which is solved by a spea ...

optimization through simulation is considered anefficient tool in dealing with optimization problems. it’s a tool that iscapable to cover real world problems to a much more complete way than the otheroptimization tools. the problem of this tool is the necessity of heavyprocessing computations. this problem is caused not only since the simulationprocess is time consuming, but the objective funct...