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The vast application of energy from different resources in agricultural production has resulted in negative environmental consequences. The importance of food security and sustainable production is undeniable therefore finding appropriate solutions to meet world's food requirements from one hand and environmental requirements from the other hand has become an interesting topic in the recent decades. Evolutionary algorithm (EA) can be employed in these problems because they can simultaneously focus on two or more objective functions. Multi-objective genetic algorithm (MOGA) as one of the EAs was selected and wheat as one of the most important strategic crops was chosen in order to test the application of these algorithms in farm systems. MOGA was employed to find the best mix of agricultural inputs which can be able to minimize greenhouse gas emissions and maximize output energy and benefit cost ratio simultaneously. The results revealed that on average 41% of the total energy input can be reduced and simultaneously, 68% of the total greenhouse gas emissions (GHG) emissions can be decreased. The outcomes demonstrated that on average a total amount of 28024 MJ energy from different sources is needed for wheat cultivation in the region while in the present condition on average an amount of 47225 MJ per ha is consumed. This amount of energy is responsible for 4217 kg CO 2 while it can be reduced to the value of 1502 kg CO 2 per ha wheat cultivation. The outcomes of the present study showed the valuable application of multi-objective genetic algorithm for optimization of energy consumption in wheat cultivation.

The main aim of the investigation was to predict chip form based on machining parameters and surface roughness. Straight turning of mild steel and AISI 304 stainless steel were performed. Spindle speed, feed rate, depth of cut and surface roughness of the material were used as inputs. Computational intelligence techniques could be used for the prediction process. In this article support vector regression (SVR) was applied for the chip form prediction. The SVR model was compared with other computational intelligence models like artificial neural network (ANN) and genetic programing (GP) techniques as benchmark models. The crucial aim of the study was to predict favorable and unfavorable chip form according to the machining parameters. By the way one should make optimal machining conditions in order to avoid unfavorable chip form. Based on the results, SVR (R 2 : 0.9682) model outperformed ANN (R 2 : 0.8367) and GP (R 2 : 0.7753) model for the chip form prediction..