The paper describes a new way to tackle a combined wind farm layout and wake steering optimization problem by applying a genetic algorithm. With some clever methods to handle mutation and crossover, it is able to produce better results than with a basic implementation available in PyGAD, which has trouble converging. Moreover, the paper builds on the work of Stanley et al. (2023) to avoid a nested optimization and does so by proposing a new formulation for the geometric yaw relation. The AEP gains are in the order of 0.4%, which is significant for a commercial wind farm and worth pursuing. The authors explain the method very thoroughly and included a couple of helpful drawings to help the reader understand at key points in the story. It is also very valuable that the results are presented in a statistical fashion to show the random nature of the algorithms.

Nevertheless, I would like to make some suggestions to improve the paper. First, it would be good to explain (qualitatively) how the geometric yaw expressions depend on the assumed wake shape and possibly the wind turbine type. This could explain why the expression from Stanley et al. (2023) does not translate well to this study. The tuning parameter γ_max could also benefit from some introduction, especially since it can be linked to physical actuation limits and may be a first step towards a multi-objective optimization with structural loads.

Furthermore, with 36 wind direction bins (i.e., 0, 10, …, 350°), there may be a danger that the optimizer is gaming the cost function by orienting the turbines along the edges of the wind direction bins (i.e., along 5, 15, …, 355°). Since the AEP increases are only < 0.5%, this could play a role. Have the authors checked the resulting layouts for such artefacts and whether they impact the result significantly?

The discussion section would benefit from some reflection on the 15-W/m² saturation point. Particularly whether it is a universal limit or case-specific – would one expect the same with different wind farm plot shapes, or is this simply driven by the turbine spacing and how much a wake can actually be deflected from the downwind turbine?

Some other minor points that may be considered for a next revision:

• There are a couple mix-ups between US vs. British English (e.g., recognize vs. recognise, favorable vs. favourable).
• For one who wants to reproduce the work, it would be helpful to add a reference to the HKN site conditions shown in Fig. 8 (they are public).
• The shaded areas in Fig. 10 are very light and difficult to read from screen.
• It is very difficult for a reader who quickly browses through the paper to understand what is meant with “Std of the probability of occurrence” (Fig. 16). I would suggest renaming the axis label to “wind direction variability” and/or spent a line in the caption to explain.
• It would be helpful to state what happens with the area of the wind farm plot under different power densities and higher numbers of turbines in Section 6.4.1 and 6.4.2, respectively. Now it is not immediately clear whether the wind farm plot increases in size with an increasing number of turbines.
• I feel that renaming the sections 6.4.2 and 6.4.3 to simply "Number of turbines" and "Wind direction variability" woud reflect the contents better. A browsing reader could also connect those better to the axis labels of Fig. 15 and 16.

Overall, nice work and I hope to see the final paper soon!

Thank you for this contribution!  In general, writing is well done and figures are clear.  References to literature is thorough.

The innovation of optimizing for the coupled condition that wake steering is or isn't applied to the co-designed farm is a valuable contribution.

Figure 17: I understand the pareto front for the multi-objective points, and for the co-design points, I understand they improve with wake steering, but lead to losses without.  The sequential points are more mysterious to me, there are some that appear to lose AEP with and without wake steering applied?

The algorithm for the geometric yaw of Stanley is also implemented and publicly availble within FLORIS: (see for example here: https://github.com/NREL/floris/blob/main/floris/optimization/yaw_optimization/yaw_optimizer_geometric.pyand here: https://nrel.github.io/floris/examples/examples_control_optimization/006_compare_yaw_optimizers.html)

This could be interesting to point out.  Figure 15 also gives the impression that these improvements can be fairly critical, and perhaps one of the improved algorithms from this paper could be submitted as a pull request back to FLORIS.