# New Approach of Blind Adaptive Equalizer Based on Genetic Algorithms
## 2 Related Works
[24] proposes an adaptive blind equalization method using genetic algorithms (GAs) and a Linear Prediction Error (LPE) filter to improve the frequency response of voltage and current transformers for harmonic measurement in power systems. This approach effectively reduces ratio and phase errors, meeting IEC standards. Similarly, ref. [34] introduces a modified bat algorithm integrated with artificial neural networks (ANNs) for nonlinear channel equalization in wireless systems. By optimizing ANN parameters through population-based techniques inspired by bats' echolocation, the method achieves superior mean square error (MSE) and bit error rate (BER) performance, effectively mitigating noise and nonlinear distortions. Meanwhile, ref. [23] presents a Uni-Cycle genetic algorithm (GA) for optimizing adaptive linear equalizer coefficients, employing a single GA generation per signaling interval to reduce computational costs while maintaining fast MSE convergence and robust tracking under Doppler shift conditions. While the method in [24] focuses on addressing transformer frequency response issues in power systems, BLE-GA targets broader challenges in communication systems. Similarly, refs. [23; 34] prioritize efficient parameter optimization and robust tracking in nonlinear and time-variant channels, respectively, whereas BLE-GA offers a unified framework that balances scalability, adaptability, and noise resilience, providing complementary advancements to adaptive equalization techniques in diverse domains. The articles [27; 28] explore advanced adaptive equalization techniques for specific communication systems. Ref. [27] introduces SkipNet, an adaptive neural network-based equalization framework for upstream 100 Gbit/s PAM4 passive optical networks (PONs). By integrating pre-trained nonlinear kernel networks with adaptive output layers usingthe least mean square (LMS) algorithm, SkipNet achieves rapid reconfiguration and robust performance against impairments such as semiconductor optical amplifier (SOA) nonlinearities and fiber dispersion. It outperforms traditional feed-forward and fixed neural network equalizers in dynamic range and dispersion tolerance while adhering to ITU-T standards. Similarly, ref. [28] applies deep learning (DL) and adaptive equalization algorithms (AEAs) to improve railway communication systems using high-frequency visible light communication (VLC). This approach combines convolutional neural networks (CNNs) for feature extraction with wavelength division multiplexing (WDM) and direct current-biased optical orthogonal frequency division multiplexing (DCO-OFDM), achieving enhanced transmission rates and reduced inter-symbol interference (ISI). A fuzzy C-means clustering-based unsupervised learning method further reduces bit error rates (BERs) to 0.0001, effectively mitigating signal distortion. Unlike SkipNet, which addresses challenges in optical networks with rapid adaptability to specific impairments, BLE-GA focuses on broad applicability and optimization in diverse communication systems. Similarly, while [28] combines DL and AEA to enhance VLC for railway systems, BLE-GA provides a unified solution for adaptive equalization in noise-prone environments, offering complementary advancements in scalability and efficiency across different applications. The articles [35; 36] propose advanced blind equalization techniques tailored for specific communication challenges. Ref. [35] introduces a parallel blind adaptive equalization method based on the Improved Block Constant Modulus Algorithm with Decision-Directed Mode (IBCMA-DD) for high-speed satellite communications. By combining block processing under the minimum mean square error (MMSE) criterion with a decision-directed mode, the approach enhances convergence speed and reduces residual errors in nonlinear group delay channels. Its parallel structure improves spectrum efficiency and minimizes inter-symbol interference (ISI) without requiring training sequences. Similarly, ref.