## 1 Introduction  
The number, size and complexity of wind farms have been steadily increasing over the past few years as consequence of the ambitious target of producing a larger share of energy from the wind [1; 2; 3]. This has resulted in a remarkable development in the design of large-scale wind energy conversion systems, which in turn have improved the wind energy economies of scale. Proper wind farm planning has major positive economic impacts for both wind farm investors and developers. However, the problem is not only complex from the technological point of view, but is also subject to an undesirable degree of uncertainty that affects the potential profitability of the project during its expected operational lifetime. As a consequence of the inherent risk, both the required investments and the payback period might be too large to the point of economic unviability. Therefore, detailed planning, modeling and optimization are required to guarantee both technical feasibility and financial competitiveness compared to other conventional forms of energy conversion. In the early 1970s, important factors such as the worldwide social pressure for denuclearization spurred governments, utilities and the scientific community to explore the potential of wind power for large-scale electricity generation. The first scientific works related to the interactions of Wind Turbines (WTs) in arrays appeared in 1974 [4; 5; 6; 7; 8; 9; 10], marking the beginning of successive paradigm shifts in WT wake (see Section 2.2.1 for a detailed description) modeling and WT array layout optimization. The interactions between WTs through wakes were initially modelled as an equivalent increase in surface roughness which modified the Earth's boundary layer due to the WT-induced drag. Based on this coarse wake-interaction modeling, it was concluded that quite large separations between WTs were required to avoid significant wake-based energy losses. In the subsequent years, staggered equally-spaced (or grid-like) sitting schemes were slowly adopted as a rule-of-thumb by wind farm designers [11; 12; 13; 14; 15; 16; 17]. In the late 1970s and the early 1980s, just after the construction of the world's first electricity-generating wind farm in New Hampshire (USA) in 1980, the first engineering wake models [18; 19; 20; 21; 22; 23; 24], mostly based on flow momentum conservation and linearized far wake expansion assumptions (with the notable exception of the [PERSON] [25] and [PERSON] [26] models), appeared, laying the groundwork for the first study on Wind Farm Design and Optimization (WFDO) [27], which aimed at optimizing the spacing between WTs in a linear array by using a generalized reduced gradient method that maximized the total power conversion of the array. The maximum achieved increase in power conversion for the given generic study case was 1.29% compared to an equally-spaced linear array. At first glance, this might appear to be a poor improvement, however, a mere one percent improvement in electricity output would result in large additional revenues over the typical 20-year lifetime of wind farms. Seven years later, the classic work of [PERSON] _et al._[28] on WFDO appeared, leading to significant improvements in both the description of the wind farm performance (the minimized objective metric was the cost of power conversion--see Section 4.5 for misconceptions) and the numerical approach to tackle the problem (a population-based metaheuristic; the SimpleGenetic Algorithm or SGA). After that, many authors have performed a significant amount of contributions of variable complexity along two main tracks: (1) a track related to improving the performance modeling of wind farms (_i.e._, by considering the inclusion of complex sub-models for financial prospection, aerodynamics and airflow physics, structural fatigue and degradation, electrical system and grid interconnection, environmental impact and uncertainty among others); and (2) a track related to the development and testing of different numerical solution procedures.