## 1 Introduction
### Contributions and Organization of the Paper  
In this manuscript, we propose a neuroevolution of augmenting topologies (NEAT)-based adaptive beamforming scheme to increase the communication range in V2V-intersection scenarios susceptible to shadowing. Due to NEAT being an unsupervised machine learning technique, its performance is compared with other such algorithms, in particular PSO and genetic algorithm (GA) approaches, as well as to a baseline isotropic antenna. Beamforming is one of the areas where not much is known about the search space, but the objective is clear. Several works have focused on generating and optimizing beam shapes using GAs, showing good results with different types of antenna arrays, e.g., linear [19,20], planar [21,22], T-shaped [23], and 3D arrays [24]. The main goal in [19, 20, 21, 22, 23] was to minimize the side lobes' power, while in [24], the desired beam shape was generated. The specific beam shapes' generation has been required in mobile communications, in order to adapt to the position of users. Therefore, smart antennas' control has also been assisted by GA and PSO [25].  
In contrast, NEAT uses genetic algorithms to train both the weights and topologies of neural networks. The algorithm was originally proposed to solve the pole balancing problem [26] and has been used in a variety of problems such as controlling robotic arms, learning countermeasures in fighting games, and generating dynamic congestion control algorithms for the transmission control protocol [27, 28, 29]. The success of NEAT in a variety of problems can be attributed to the capacity the algorithm has to generalize solutions to complex problems.  
As can be seen, there is a clear lack of work done in the area of using beamforming in V2V communications to improve communications in urban scenarios, and there are clear reasons to work in this area. In this work, we looked to solve this problem by using algorithms that learn the antenna excitation from its surroundings. In particular, we employed genetic algorithms (GAs) and neuroevolution of augmenting topologies (NEAT). The algorithms were trained and tested in different simulation scenarios focused in urban V2V communications. The main contributions of this manuscript are summarized as follows:  
1. We show the positive impact of using evolutionary algorithms with beamforming in urban V2V communication scenarios to learn beam shapes according to the surrounding environment.
2. We propose the use of a neuroevolution algorithm to optimize beam shapes with beamforming for V2V communications in urban scenarios. Unlike other machine learning approaches, such as GA and PSO, NEAT does not require interpolating previously visited positions, since the artificial neural networks take any position as an input.
3. Beamforming with NEAT outperforms the baseline isotropic antenna, as well as beamforming optimized with MSCPSO and GA, in terms of the average response time and the communication range, which are of vital importance for road safety applications.  
The remainder of the work is structured as follows. Section 2 presents the details of the models used for the analysis and the evaluations of the proposed schemes. Section 3 describes the optimization algorithms implemented in the work, whereas Section 4 depicts the results and respective analysis. Finally, conclusions are presented in Section 5.