In recent years, there has been a growing interest for the experimental analysis in the field of evolutionary algorithms. It is noticeable due to the existence of numerous papers which analyze and propose different types of problems, such as the basis for experimental comparisons of algorithms, proposals of different methodologies in comparison or proposals of use of different statistical techniques in algorithms' comparison.

In this paper, we focus our study on the use of statistical techniques in the analysis of evolutionary algorithms' behaviour over optimization problems. A study about the required conditions for statistical analysis of the results is presented by using some models of evolutionary algorithms for real-coding optimization. This study is conducted in two ways: single-problem analysis and multiple-problem analysis. The results obtained state that a parametric statistical analysis could not be appropriate specially when we deal with multiple-problem results. In multiple-problem analysis, we propose the use of non-parametric statistical tests given that they are less restrictive than parametric ones and they can be used over small size samples of results. As a case study, we analyze the published results for the algorithms presented in the CEC'2005 Special Session on Real Parameter Optimization by using non-parametric test procedures.

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A study on the use of non-parametric tests for analyzing the evolutionary algorithms' behaviour: a case study on the CEC'2005 Special Session on Real Parameter Optimization

Nickel-based superalloy is widely employed in aircraft engines and the hot end components of various types of gas turbines with its high strength, strong corrosion resistance and excellent thermal fatigue properties and thermal stability. However, nickel-based superalloy is one of the extremely difficult-to-cut materials. During the machining process, the interaction between the tool and the workpiece causes the severe plastic deformation in the local area of workpiece, and the intense friction at the tool–workpiece interface. The resulting cutting heat coupled with the serious work hardening leads to a series of flaws, such as excessive tool wear, frequent tool change, short tool life, low productivity, and large amount of power consumption etc., in which the excessive tool wear has become one of the main bottlenecks that constraints the machinability of nickel-based superalloys and its wide range of applications. In this article, attention is mainly focused on the tool wear characteristics in the machining of nickel-based superalloys, and the state of the art in the fields of failure mechanism, monitoring and prediction, and control of tool wear are reviewed. The survey of existing works has revealed several gaps in the aspects of tool self-organizing process based on the non-equilibrium thermodynamics, tool wear considering the tool nose radius, thermal diffusion layer in coated tools, tool life prediction based on the thermal–mechanical coupling, and industrial application of tool wear online monitoring devices. The review aims at providing an insight into the tool wear characteristics in the machining of nickel-based superalloys and shows the great potential for further investigations and innovation in the field of tool wear.

Tool wear characteristics in machining of nickel-based superalloys

Review of utilization of genetic algorithms in heat transfer problems

Tuncay Erzurumlu

Papers

Application of response surface methodology in the optimization of cutting conditions for surface roughness

Comparison of the warpage optimization in the plastic injection molding using ANOVA, neural network model and genetic algorithm

Application of Taguchi optimization technique in determining plastic injection molding process parameters for a thin-shell part

Prediction of minimum surface roughness in end milling mold parts using neural network and genetic algorithm

Minimization of warpage and sink index in injection-molded thermoplastic parts using Taguchi optimization method