# New Approach of Blind Adaptive Equalizer Based on Genetic Algorithms
## 3 Methods  
### Background on Genetic Algorithms  
Genetic algorithms (GAs) are a stochastic method of intelligent search, where individuals are represented by chromosomes and compete for resources and possibilities for replication. Individuals that are more successful in competitions are more likely to reproduce than those with lower performance. The genes of the individuals evaluated spread well throughout the population, so that they can be improved and can generate increasingly appropriate offspring. The GA algorithm was introduced in [37] and widely popularized through works such as [38]. The GAs are modeled from an optimization problem, in which a trial solution to the problem is constructed in the form of a suitably encoded string of model parameters, called \begin{table}
\begin{tabular}{l l l l} \hline
**Ref.** & **Method** & **Method Description** & **Summary** \\ \hline
2022 [31] & Meta-heuristic & Blind adaptive equalization based on a third-order polynomial cost function. & Introduces MSE POLY3 to reduce ISI in QAM systems, outperforming MMA in 16 QAM. \\ \hline
2020 [25] & Deep Learning & Adaptive neural network equalizer using semi-supervised learning and Aug-VAT loss. & Proposes AdaNN, improving BER and noise resilience without requiring labeled data online. \\ \hline
2022 [26] & Deep Learning & Blind equalization and channel estimation using variational autoencoders (VAEs). & Utilizes VAEs to address variable channels and advanced modulation, achieving near-MMSE performance. \\ \hline
2023 [32] & Meta-heuristic & Fractionally spaced equalizer with a feedback loop and MDD algorithm for phase correction. & Focuses on CFO correction in UAV QAM systems, reducing ISI with a feedback architecture. \\ \hline
2022 [33] & Algo. Heuristic & Variable Modulation-Decision-Directed LMS using QPSK-like pilot signals. & Optimizes equalization for variable modulations, reducing BER and achieving high operational frequencies. \\ \hline
2023 [24] & Meta-heuristic & Blind equalization for harmonic measurements using LPE filters and GAs for stability. & Improves transformer frequency response under colored noise, meeting IEC standards. \\ \hline
2024 [27] & Deep Learning & Neural network equalizer with pre-trained kernels and adaptive LMS output layers. & SkipNet rapidly reconfigures PAM4 PON systems, outperforming fixed equalizers in dispersion and range. \\ \hline
2024 [28] & Deep Learning & Combines a CNN for feature extraction with WDM and DCO-OFDM modulation for VLC. & Proposes DL-AEA to reduce ISI in railway VLC, achieving BER of 0.0001 with unsupervised learning. \\ \hline
2023 [35] & Algo. Heuristic & Parallel blind equalization using IBCMA-DD for nonlinear satellite channels. & Improves spectral efficiency and reduces ISI in satellite systems without training sequences. \\ \hline
2024 [36] & Algo. Heuristic & Modified CMA and GMCMA with MNM for NB-IoT systems with high-order QAM. & Optimizes blind equalization in NB-IoT, reducing BER and increasing convergence speed. \\ \hline
2022 [34] & Meta-heuristic & Modified bat algorithm with an ANN for nonlinear channel equalization. & Integrates optimization inspired by ecology to improve equalization in noisy nonlinear channels. \\ \hline
2021 [23] & Meta-heuristic & Uni-Cycle GA for adaptive equalizer coefficient optimization during & Reduces computational costs while maintaining robustness in time-variant channels. \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of methods and summaries for references. an individual. A collection of individuals is in turn called a population. The main idea of a GA is to evolve toward the best solutions (individuals). Figure 1 shows a simplified scheme of the this evolutionary process. It starts from an initial population of random individuals and continues for many generations. At each generation, the fitness function is used to evaluate the entire population.