<![CDATA[The rapid development in the field of Computational Intelligence (CI) has driven the use of various techniques to solve complex problems. Two main approaches that are often compared within CI are Artificial Neural Networks (ANN) and Evolutionary Algorithms (EA), each with its own strengths and limitations. Artificial Neural Networks, inspired by the structure of the human brain, operate through interconnected layers of neurons and have proven effective in pattern recognition and non-linear data modeling. Meanwhile, Evolutionary Algorithms, inspired by the process of biological evolution, are used for global solution searches in complex optimization problems without requiring mathematical derivatives of the objective function. In this study, we compare these two techniques based on architecture, model complexity, performance, and their applications across various domains. Additionally, we explore the potential of integrating both techniques into a hybrid approach that can optimize performance on more complex problems. The findings of this study indicate that combining ANN and EA, such as in neuroevolution approaches, provides more adaptive and efficient solutions compared to using each technique independently. This study offers insights into the use of ANN and EA and their applications in image processing, industrial optimization, and data-driven intelligent systems.]]>
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