The art of computer programming, volume 1 (3rd ed.): fundamental algorithms
The art of computer programming, volume 1 (3rd ed.): fundamental algorithms
A survey of fast exponentiation methods
Journal of Algorithms
Handbook of Applied Cryptography
Handbook of Applied Cryptography
CRYPTO '89 Proceedings of the 9th Annual International Cryptology Conference on Advances in Cryptology
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Finding Minimal Addition Chains with a Particle Swarm Optimization Algorithm
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
A simulated annealing algorithm for the problem of minimal addition chains
EPIA'11 Proceedings of the 15th Portugese conference on Progress in artificial intelligence
Interactive visualization applets for modular exponentiation using addition chains
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
A mutation-selection algorithm for the problem of minimum brauer chains
MICAI'11 Proceedings of the 10th international conference on Artificial Intelligence: advances in Soft Computing - Volume Part II
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Since most public key cryptosystem primitives require the computation of modular exponentiation as their main building block, the problem of performing modular exponentiation efficiently has received considerable attention over the years. It is known that optimal (shortest) addition chains are the key mathematical concept for accomplishing modular exponentiations optimally. However, finding an optimal addition chain of length r is an NP-hard problem whose search space size is comparable to r !. In this contribution we explore the usage of a Genetic Algorithm (GA) approach for the problem of finding optimal (shortest) addition chains. We explain our GA strategy in detail reporting several promising experimental results that suggest that evolutionary algorithms may be a viable alternative to solve this illustrious problem in a quasi optimal fashion.