In machine learning, ensemble techniques are widely used to improve the performance of both classification and regression systems. They combine models generated by different learning algorithms, typically trained on data subsets or with parameters, obtain more accurate models. Ensemble strategies range from simple voting rules complex effective stacked approaches. based adopting a meta-learner,...

yard crane is an important resource in container terminals. efficient utilization of the yard crane significantly improves the productivity and the profitability of the container terminal. this paper presents a mixed integer programming model for the yard crane scheduling problem with non- interference constraint that is nphard in nature. in other words, one of the most important constraints in...

this paper presents a framework for long term transmission expansion planning in competitive, electricity markets. transmission lines and phase shifters are taken into account as expansion options. maximization of the network users' benefits, with satisfying security constraints are considered as the criterion for transmission expansion planning. the elements of the objective function are the...

We present SLUG, a method that uses genetic algorithms as wrapper for programming (GP), to perform feature selection while inducing models. This is first tested on four regular binary classification datasets, and then 10 synthetic datasets produced by GAMETES, tool embedding epistatic gene-gene interactions into noisy datasets. compare the results of SLUG with ones obtained other GP-based metho...