# New Approach of Blind Adaptive Equalizer Based on Genetic Algorithms
## 2 Related Works  
The articles [31, 32, 33] explore diverse approaches to adaptive equalization in distinct contexts. Ref. [31] introduces the MSE POLY3 method, a blind adaptive equalization technique based on a third-order polynomial cost function optimized with the mean square error (MSE) criterion. This method minimizes residual inter-symbol interference (ISI) in noisy QAM systems, outperforming the Multi-Modulus Algorithm (MMA) for 16 QAM modulation within specific signal-to-noise ratio (SNR) ranges. Similarly, ref. [32] proposes the Offset Feedback Fractionally Spaced Equalization (OF-FSE) architecture, addressing carrier frequency offset (CFO) challenges in QAM-based UAV communication systems. OF-FSE integrates a feedback loop for real-time phase error correction and employs the Multi-Modulus Decision-Directed (MDD) algorithm for enhanced convergence and reduced ISI. Meanwhile, ref. [33] focuses on the Variable Modulation-Decision-Directed Least Mean Square (VM-DDLMS) algorithm, which improves stability and performance in variable modulation systems by utilizing QPSK-like pilot signals for equalization. VM-DDLMS achieves faster convergence and superior bit error rate (BER) performance, particularly for higher-order modulations, and demonstrates efficiency in hardware implementations with an operating frequency of up to 333.33 MHz. The BLE-GA introduced in this study offers a unified framework for addressing challenges in stochastic environments and higher modulation schemes, such as 64 QAM. Unlike MSE POLY3, which focuses on MSEoptimization in QAM systems, BLE-GA emphasizes computational efficiency and scalability through a block-based architecture. Similarly, while OF-FSE targets phase correction and ISI reduction in UAV communications, BLE-GA focuses on adaptability across dynamic and noise-prone systems. Finally, compared to VM-DDLMS, which eliminates the need for dynamic parameter adjustments in variable modulation scenarios, BLE-GA applies genetic algorithms to achieve robust performance across a broader range of communication environments, offering complementary strategies for adaptive equalization. The articles [25; 26] explore innovative techniques for adaptive equalization in optical communication systems. Ref. [25] introduces AdaNN, an adaptive neural network-based equalizer employing semi-supervised learning. Through a sliding window for unlabeled data collection and an augmented virtual adversarial training (Aug-VAT) loss function, AdaNN accelerates convergence and enhances noise resilience without requiring labeled data during the online phase. Experimental results demonstrate superior bit error rate (BER) performance compared to maximum likelihood sequence estimation (MLSE) and non-adaptive neural network equalizers across diverse optical link scenarios. Similarly, ref. [26] explores the use of variational autoencoders (VAEs) for blind equalization and channel estimation in coherent optical systems. It introduces VAE-NN and VAE-LE architectures, leveraging variational inference for higher-order modulation formats, probabilistic constellation shaping (PCS), and dual-polarization signals. VAE-LE achieves performance comparable to the optimal non-blind MMSE equalizer and surpasses traditional methods like the Constant Modulus Algorithm (CMA) in PCS scenarios. Unlike AdaNN, which excels in noise resilience and semi-supervised learning, BLE-GA focuses on scalability and optimization in dynamic and noise-prone communication systems. Similarly, while the VAE-based methods in [26] target advanced modulation formats and probabilistic shaping in coherent optical systems, BLE-GA provides a complementary approach by addressing equalization challenges in broader contexts, emphasizing computational cost and convergence efficiency. The articles [23; 24; 34] present diverse advancements in adaptive equalization methodologies. Ref.