2020/12/22 by Adrian Korban, Korban, Adrian, Serap Şahinkaya +3 · 1 citation
Computer Science · Engineering · #Coding theory and cryptography #Cooperative Communication and Network Coding #FOS: Computer and information sciences #Information Theory (cs.IT) #Neural and Evolutionary Computing (cs.NE) #graph theory and CDMA systems
paper · pdf · doi:10.48550/arxiv.2012.12248
openalex publication_date 2020/12/22 · openalex created_date 2021/01/05 · openalex updated_date 2026/07/28
In this paper, a genetic algorithm, one of the evolutionary algorithms optimization methods, is used for the first time for the problem of finding extremal binary self-dual codes. We present a comparison of the computational times between a genetic algorithm and a linear search for different size search spaces and show that the genetic algorithm is capable of finding binary self-dual codes significantly faster than the linear search. Moreover, by employing a known matrix construction together with the genetic algorithm, we are able to obtain new binary self-dual codes of lengths 68 and 72 in a significantly short time. In particular, we obtain 11 new extremal binary self-dual codes of length 68 and 17 new binary self-dual codes of length 72.