## 2 Research Focus and Relevance
However, many research reports show that the complexity revealed by most of the design problems makes identifying a global optimum solution of a concept impossible [16; 17], with the goal of concept design being limited to improvement issues rather than quantitative optimization of design concepts [33; 46]. Even if the quantitative optimization problems, in meeting multiple targets during the conceptual design phase, are difficult to achieve for complex design problems, defining solutions close to optimum is desirable. Therefore, various evolutionary algorithms have been developed and used to meet this goal [46]. When some partial solutions are formulated, these algorithms aim to automate at a certain extent the conceptualization process. Several works demonstrate this conduit. Some relevant references in this respect are further highlighted. For example, [41] uses functional reasoning and a pool of building blocks to conceptualize an engineering solution to a given design subject. Here, conceptualization is more based on combinations of predefined blocks to fit some boundaries, as in a puzzle, without inducing a creative input in this operation. Paper [49] uses particle swarm optimization (PSO) to define an optimal production equipment configuration using a limited set of modules from a library. The best local solution is thus obtained, but using a \"frozen\", \"mechanistic\" space of intervention, in which no creative support of engineers is brought during solution formulation. In the same spirit, the work reported by [42] exploits genetic algorithms to handle this type of problem. Comparable evolutionary algorithms are considered by [43; 50] that automate the conceptualization of a particular engineering design problem. In this register, the work conducted by [34] can also be reported. These algorithms run on a computer without human interaction in this process; thus, the optimization problem does not infuse any human input during the incremental product conceptualization process. This is a limitation because we reject from the algorithm an extremely valuable resource, the human expertise. Conceptually speaking, this rejection contradicts the core law of ideality highlighted in [32]. Despite the long history of engineering design, a conclusion coming up with clarity from the current developments in this field is that no universal framework and tool exists today as a \"panacea\" for tackling conceptual design [14; 35]. Furthermore, there are still insignificant contributions and know-how in combining qualitative improvement/optimization with quantitative improvement/optimization in conceptual design. Nevertheless, throughout the product conceptualization process, many design problems require a balanced intercorrelation between qualitative optimization, led by structured creativity/innovation, and quantitative optimization, led by optimal design algorithms to efficiently direct design efforts towards highly effective solutions. Narrowing the space of investigation to the design algorithms for reconfiguration, we can report that most of the cases focus on control algorithms or on reconfigurable manufacturing systems, and fewer are directed towards designing reconfigurable machine tools. Those related to reconfigurable machine tools put a clear emphasis on modularity; thus, being too sectorial for our research. Searching in Clarivate Analytics with the keyword \"reconfigurable machine tool\", 128 references are displayed. Refining the search with the word \"algorithm\", references are reduced to 21, from which only 5 papers introduce algorithms for reconfigurable machine-tool design. Searching in the Scopus database with the same combination of keywords, 25 papers are returned, from which only 1 paper is referring to reconfigurable machine-tool design and is new to those identified in Clarivate Analytics. The first work referenced here is [49]. It formulates a design problem by considering three conflicting parameters: configurability, cost, and process accuracy.