While deep neural networks (DNNs) deliver state-of-the-art accuracy on various applications from face recognition to language translation, it comes at the cost of high computational and space complexity, hindering their deployment edge devices. To enable efficient processing DNNs in inference, a novel approach, called Evolutionary Multi-Objective Model Compression (EMOMC), is proposed optimize ...

In this research, the multi-objective project management decision problem with fuzzy goals and fuzzy constraints are considered. We constitute α-cut approach and two various fuzzy goal programming solution methods for solving the Multi-Objective Project Management (MOPM) decision problem under fuzzy environments. The Interactive fuzzy multi-objective linear programming (i-FMOLP) and Weighted Ad...

Customers and consumers are the necessities for the survival of industries and organizations. Trying to improve the process in order to increase consumer satisfaction is the most important aim. The survival of an organization depends on its ability to continue the activities in compliance with the demands of customers to meet their legitimate needs. An organization is successful when it exactly...

It is well-known that in many safety critical applications safety goals are antagonistic to other design goals or even antagonistic to each other. This is a big challenge for the system designers who have to find the best compromises between different goals. In this paper, we show how model-based safety analysis can be combined with multi-objective optimization to balance a safety critical syst...

Multi-Objective Evolutionary Algorithms (MOEAs) have been proved efficient to deal with Multi-objective Optimization Problems (MOPs). Until now tens of MOEAs have been proposed. The unified mode would provide a more systematic approach to build new MOEAs. Here a new model is proposed which includes two sub-models based on two classes of different schemas of MOEAs. According to the new model, so...

We describe a new variable selection procedure for categorical responses where the candidate models are all probit regression models. The procedure uses objective intrinsic priors for the model parameters, which do not depend on tuning parameters, and ranks the models for the different subsets of covariates according to their model posterior probabilities. When the number of covariates is moder...