Constraint-Preserving Genetic Algorithms for Embedding Linear Codes into Self-Orthogonal Codes

2026-09-02

AI Summary

本文使用基于遗传算法的启发式框架，通过约束保持的交叉和变异操作来构造最优自正交码，解决了线性码嵌入自正交码的问题。

Abstract

In this paper, we aim to construct binary optimal self-orthogonal codes using shortest self-orthogonal embedding methods. For this purpose, we design a heuristic framework based on a genetic algorithm. We explore the search space of shortest self-orthogonal embeddings using a fitness function based on the minimum distance and the number of minimum-weight codewords. We construct \emph{constraint-preserving} crossover and mutation operations so that every chromosome yields a valid self-orthogonal embedding, while high-fitness structural features, such as favorable subsequences of orthogonal generators, are propagated across generations. We also analyze the time and storage complexity of the algorithm, and validate our design through an ablation study on guided crossover and a comparison with random search under an equal time budget. Using this method, we obtain $66$ new binary optimal self-orthogonal codes that meet the upper bound, together with $135$ further self-orthogonal codes attaining the best minimum distance found so far.

Problem

• self-orthogonal codes
• genetic algorithms
• embedding
• minimum distance

Innovation

• genetic algorithm
• self-orthogonal codes
• constraint-preserving
• crossover and mutation
• fitness function