## 1 Introduction  
This work is a continuation of a previous paper, presenting a preliminary design methodology for axial turbocharger turbines [1]. The computational resource increment in recent years has allowed multidisciplinary design optimisation (MDO) to permeate multiple R&D disciplines, to the point of becoming essential in aerodynamic shapes optimization [2; 3]. Traditionally, the design process required a high degree of engineering involvement and was reliant on designer experience in the field. Over the past 20 years, with the improvement in computational resources and in fluid dynamics algorithms accuracy, the trend in research has shifted to a predominantly numerical approach: \"In regard to human interface, MDO developers and users seem to have arrived at a consensus that computer-based MDO methodology is an increasingly useful aid to the creative power of human mind\" [4]. In the specific case of turbines, a multidisciplinary approach is necessary as changes in blade geometry lead to variations in both structural and aerodynamics results, rendering it impossible to obtain realistic geometries without taking both fields into consideration. Design optimisation processes are largely portable across similar applications, thus research can be conducted on optimisation techniques in axial turbines for different applications. Multiple sources are present, however, it is clear that the design optimisation topic is highly sensitive to manufacturers,with the majority of the sources being obsolete [5; 6], as highlighted by the most recent paper on the topic [7]. The most notable common trait in previous examples of design optimisation is the fact that most of them were carried out using genetic algorithms (GAs). The method is one of the most popular in MDO, as it can handle \"discontinuities, multimodality, disjoint feasible spaces and noisy function evaluations\" [8], which are common features in the optimisation task to be carried out, as most of the objectives are effectively described by discontinuous functions. These capabilities are what distinguish GAs from its competitors. In recent years, research on this relatively new topic has been extremely prolific, with numerous innovations and schools of thoughts developing at an incredible pace. In 2018 alone, more than 20,000 papers or books have been published on the topic of genetic algorithm optimisation [9]. The general consensus gauged from surveys [10; 11] is that methodologies and implementations are still in an embryonal stage, and that there is no clear best implementation of the method. The authors' conclusions are that techniques should be evaluated in a case-by-case manner. Recent literature focuses on theoretical of purely numerical approaches, especially in a manufactural design optimisation task. The complexity and time-consuming nature of MDOs featuring multiple evaluations, however, is preventing any complete (i.e., from preliminary to final) design optimisation tasks relevant for this project. Research has followed two different approaches, either reducing the objectives to optimise for [12], or accepting less accurate results by utilising simpler mathematical models [13]. The first example of axial turbine parameter optimisation available was published in 1985 [5]. The algorithm, coded in FORTRAN, allowed the user to obtain a preliminary blade design with 11 independent parameters, mathematically derived by the author. The other 14 parameters were optimised by using a mathematical model. While the results were heavily approximated, this publication is of absolute importance from an historic point of view, as [PERSON], in their ingenuity, understood the need of a parametric blade generation to allow a faster preliminary design process, thus allowing engineers to apply their experience and expertise in a focussed way. The procedure features numerous approximations and essentially no integration with computational fluid dynamics (CFD), however it should be noted that the algorithm was executed on a computer with 8 kB of RAM [14].