Artificial Neural Networks (ANNs) have been widely applied in diverse domains, including image recognition [10], natural language processing [11], and medical diagnosis [12], among others. The ability of ANNs to model complex patterns makes them essential tools in machine learning. However, traditional training methods for ANNs often face challenges with local optima, which limits the network's ability to achieve a better solution. The complexity of hyperparameter tuning further exacerbates this issue, requiring extensive experimentation to find configurations that enhance performance [13]. Evolutionary algorithms (EAs) offer an alternative approach to optimizing neural networks by mimicking biological evolution through selection, mutation, and crossover operators. Neuroevolution, which integrates EAs into ANN training, optimizes neural networks by evolving a population of candidate models through genetic operators. Each iteration generates a new population where networks gradually adapt their architectures and weights based on their performance, enabling a form of evolutionary learning. This approach has demonstrated improvements in network adaptability and convergence [14; 15], as neural networks function as adaptive agents that refine their structure and parameters over time to enhance performance in a given task. Unlike conventional methods that require predefined architectures, the NeuroEvolution of Augmenting Topologies (NEAT) allows networks to grow in complexity by introducing new neurons and connections during the training phase. Additionally, NEAT employs reinforcement mechanisms to guide the evolutionary process, enabling unsupervised learning and fostering structural innovation within evolving populations [16]. Several variants of NEAT, such as HyperNEAT [17], Hybrid Self-Attention NEAT [18], and CoDeepNEAT [19], have been proposed to enhance the efficiency and scalability of neuroevolution. However, these approaches typically involve higher computational costs and increased complexity due to the need for more sophisticated search strategies and larger solution spaces. In this work, we focus on the base NEAT approach to maintain a balance between computational efficiency and model complexity. In [20], [PERSON] et al. proposed a novel Recurrent NEAT-based MUSIC (RNEAT-MUSIC) algorithm to optimize 2D-DOA estimation by reducing computational complexity without compromising accuracy. Their approach leverages the NEAT framework, which evolves neural network architectures dynamically, allowing for adaptive learning without requiring predefined structures. To further enhance efficiency, the proposed model incorporates a recurrent structure within NEAT, minimizing the number of required neural network parameters and reducing the overall computational burden. The algorithm extracts phase components from the received signal covariance matrix, applying a reduced-dimensional neural network to restrict the scanning region before executing the computationally expensive MUSIC stage. Experimental validation demonstrates that RNEAT-MUSIC achieves a 75% reduction in computational workload compared to traditional 2D-MUSIC while preserving high-resolution DOA estimation. The results indicate that this approach effectively balances exploration and exploitation in neuroevolutionary learning, improving adaptability and convergence speed. These findings position RNEAT-MUSIC as a promising alternative for real-time satellite tracking, offering a computationally efficient solution for high-precision DOA estimation in space communication applications. Despite NEAT advantages, maintaining an effective balance between exploration and exploitation remains challenging, as genetic algorithms risk premature convergence or high computational costs when optimizing complex network structures. To address these limitations, this study proposes a hybrid neuroevolutionary approach that integrates Cellular Processing Algorithms (PCELL) with NEAT-based networks.