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A Multi-Population Genetic Algorithm for Secure and Efficient Elliptic Curve Parameter Generation
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 84 |
| Year of Publication: 2026 |
| Authors: Mohammed H. Alabiech |
10.5120/ijca2026926478
|
Mohammed H. Alabiech . A Multi-Population Genetic Algorithm for Secure and Efficient Elliptic Curve Parameter Generation. International Journal of Computer Applications. 187, 84 ( Feb 2026), 53-58. DOI=10.5120/ijca2026926478
Abstract
The field of cryptography has advanced rapidly in recent years, with Elliptic Curve Cryptography (ECC) emerging as one of the most efficient methods for ensuring both security and computational efficiency. The strength of this encryption technique is largely determined by the mathematical properties of the Elliptic Curves (EC), which are governed by the constants defining its structure. This study explores the use of a Genetic Algorithm (GA)—an evolutionary artificial intelligence technique—to determine optimal values for the constants of EC, aiming to maximize the number of valid points over a finite field. This approach highlights the feasibility of applying intelligent optimization techniques to complex mathematical challenges in cryptographic system design. A prototype was implemented to simulate the process and assess the GA’s performance in identifying effective solutions. Preliminary results suggest that the GA offers a viable alternative to traditional search methods, enabling more efficient exploration of cryptographic parameters. This could contribute to designing more efficient EC for cryptographic applications and deepen our theoretical and practical understanding of EC construction.
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