## 4 Composite Optimization  
The optimization routine applied to the monocque structure leverages the dependency of composite material properties on the characteristics of its constituent lamina and core materials. By representing these laminate characteristics as variables, a parametric design genome defining a specific lamination sequence is constructed. Targeted modifications within this genome are expected to yield a structure with enhanced properties according to predefined criteria. Additionally, two distinct cases differing by the inclusion of lamina orientation angles in the variable set were examined to comprehensively assess the influence of this parameter on the results. Finally, it should be stated that the available literature from related research on the weight optimization of composites [6; 25; 26], as well as time limitations and the authors' familiarity with this method, were key elements in the adoption of that method in comparison to other alternatives. As previously noted, genetic algorithms optimize composite structures by iteratively selecting advantageous traits and eliminating inferior ones to achieve optimal outcomes aligned with predefined objectives. This process, analogous to natural selection and biological evolution--hence the term \"genetic\"--facilitates the exploration of novel design space regions to identify superior solutions. The optimization of the composite chassis structure is a resource-intensive process, systematically segmented into distinct phases, starting by formulating the problem in terms of design variables, including material type, properties, thickness, ply count per lamina, fiber orientation, and stacking sequence within the composite. Specifically, the optimization variables in this study comprise the number of plies per lamina and their orientation angles (see Table 6).Constraints were subsequently applied to the design variables to improve the simulation's fidelity to real-world conditions and ensure compliance with fabrication requirements. These included examples such as limits on the maximum number of consecutive unidirectional or woven prepreg plies to prevent resin pooling and maintain symmetrical lamination. Following the imposition of constraints on design variables, the chassis's structural performance was evaluated according to specific criteria. To minimize structural mass, the proposed designs were assessed via a single objective function representing the total monocque mass, computed as the sum of individual finite element masses. Optimal solutions from each generation were retained for subsequent iterations, while new designs were introduced, prioritizing the retention of high-performance individuals. Finally, this iterative process adopted as termination criteria a minimum depth of 200 generations and a stagnation period of 50 generations,  
Regarding the algorithm's application, a simple genetic algorithm was adopted for the mono-criterion problem. The initial parent population was set to 20 randomly generated designs, while uniform crossover and mutation operations were utilized in the procedure. To clarify the implementation, the terms \"gene\" and \"genome\" refer to a variable and the composite lamination, respectively. In the uniform crossover, each gene has an equal probability of inheritance from either parent. The 60% crossover probability in Table 7 denotes the likelihood that crossover occurs; otherwise, offspring inherit parent genomes unchanged. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Optimization Parameter** & **Domain** & & \\ \hline  & Woven CFRP plies & UD CFRP plies & A.O.P for all plies \\ (A) Number of plies per & Surface\_Ply\_1\_no\_of\_layers & UD\_1. up to UD\_6. & Surface\_Ply\_1. Angle up to \\ lamina type & up to Ply\_2\_no\_of\_layers & number\_of\_layers & Ply\_2 angle UD\_1. Angle up to UD\_6. angle \\ (B) Lamina angle of placement & & & \\ \hline \hline \end{tabular}
\end{table}