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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Bellfkih, El Mehdi
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Computer Science & IT Engineering

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New family of error-correcting codes based on genetic algorithms Bellfkih, El Mehdi; Nouh, Said; Chemseddine Idrissi, Imrane; Louartiti, Khalid; Mouline, Jamal
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1077-1086

Abstract

This paper introduces a novel error-correcting code (ECC) construction and decoding approach utilizing genetic algorithms (GAs). Classical ECCs often struggle with efficiency in correcting multiple errors due to time-consuming matrix-based encoding and decoding processes. Our GA-based method optimizes generator vectors to maximize the minimum distance between codewords, enhancing error correction capabilities. Specifically, we construct a new family of ECCs with code length 31, dimension 12, and minimum distance 7, reducing complexity from O(kn) to O(k(n−k)) by encoding message blocks with vectors instead of matrices. In the decoding phase, the GA effectively corrects errors in received codewords. Experimental results show that at a signal-to-noise ratio (SNR) of 7.7 dB, our method achieves a bit error rate (BER) of 10−5 after only 9 generations of the GA. These results demonstrate improved error correction and decoding performance compared to traditional methods. This study contributes an innovative approach using GAs for error correction, offering simpler encoding and robust performance in coding schemes.
An efficient method to improve machine learning decoders using automorphisms group Idrissi, Imrane Chemseddine; Nouh, Said; Bellfkih, El Mehdi; El Assad, Mohammed; Marzak, Abdelaziz
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp547-558

Abstract

The decoding of error-correcting codes (ECCs) is a critical aspect of communication systems, yet traditional decoding techniques can often be computationally demanding or ineffective for certain codes, necessitating innovative approaches. In this study, we introduce a hybrid approach that combines machine learning and automorphism techniques to optimize the decoding process. Specifically, we train multilayer perceptron (MLP) models to learn the mapping between error syndromes and their corresponding errors. While these models exhibit robust learning capabilities, their performance sometimes does not reach 100%. To mitigate this limitation, we exploit the automorphism group of the code—a set of structure-preserving transformations—to convert the errors that the MLP struggles to decode into ones it can process more effectively. We use a minimum number of p permutations, pre-calculating and storing all possible automorphisms to ensure computational efficiency. Our experimental results reveal that this hybrid approach substantially enhances the decoding performance of the MLP model, presenting a promising avenue for decoding ECCs. Importantly, this approach is not limited to MLP models and can be applied to any machine learning model with a learning score less than 100%, broadening its applicability and impact. By integrating machine learning with traditional algebraic coding theory, we propose a new paradigm that holds the potential to revolutionize the design of decoding systems, making them more efficient and effective.
Co-Authors Chemseddine Idrissi, Imrane El Assad, Mohammed Idrissi, Imrane Chemseddine Louartiti, Khalid Marzak, Abdelaziz Mouline, Jamal Nouh, Said