Self-Organizing Maps (SOMs) are recognized as one of the most fascinating methods within the fields of machine learning and knowledge extraction. This process, through its interdisciplinary nature, aims to discover new, useful, and reliable insights from datasets, ultimately identifying patterns and information that hold significant value. These maps are capable of projecting complex, high-dimensional relationships onto a simplified grid while retaining the topological structure of the original data. Besides, algorithms with an evolutionary sense offer an efficient means of solving intricate optimization problems across various applications. A key aspect of applying evolutionary techniques to real-world scenarios is their heavy reliance on numerous function evaluations before reaching an adequate solution. Typically, evolutionary methods generate new solutions without leveraging the valuable knowledge hidden within the vast number of previously generated solutions. By utilizing this knowledge during the evolutionary process, the search strategy could be significantly enhanced, guiding it toward more promising regions and improving convergence behavior. This chapter presents an evolutionary optimization method where the knowledge gathered throughout the process directs the search strategy. Through this method, the SOM is trained to locate the best solution's corresponding neural unit. Based on the local data from this unit, a fresh set of potential solutions is generated. SOMs serve as the knowledge extraction mechanism to narrow down the search area and detect promising regions. Therefore, in every generation, a smaller group of the overall solutions generated is chosen to train the SOM. By incorporating the extracted knowledge, the method enhances convergence toward difficult high-dimensional optima, using fewer function evaluations. Using a collection of widely recognized benchmark functions and three practical engineering challenges, numerous advanced optimization methods have been evaluated against the performance of the proposed approach.