A Novel Genetic Search Scheme Based on Nature -- Inspired Evolutionary Algorithms for Self-Dual Codes
This work provides a faster method for mathematicians and coding theorists to discover new self-dual codes, an incremental improvement for code construction.
This paper applies a genetic algorithm to find extremal binary self-dual codes, demonstrating significantly faster computational times compared to a linear search. By combining the genetic algorithm with a known matrix construction, the authors discovered 11 new extremal binary self-dual codes of length 68 and 17 new binary self-dual codes of length 72.
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.