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Genetic Algorithm AI and its role in seed phrase generation
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The genetic algorithm is a heuristic optimization method. It is based on the principles of natural selection and population evolution. The use of genetic algorithms allows for generating random combinations of seed phrases, evaluating their quality based on predefined criteria, and efficiently iterating the population for further selection of mnemonic phrases to recover access to Bitcoin wallets with potentially non-zero balances. A “random population of seed phrases” is created, which represents certain combinations of words. These combinations are called genotypes. Then each genotype is evaluated based on a criterion such as having a positive balance in the wallet.
In the next step, the best genotypes are selected based on their evaluations. This is done using “selection operators” that give preference to genotypes with higher ratings. Then comes the crossover operation, where the selected genotypes are combined to create a new generation of genotypes. In this process, there is an exchange of genetic information between genotypes, which allows for new combinations of seed phrases to be obtained. After crossover, the “mutation” operation occurs, which randomly modifies some genes in the genotypes of the new generation. This helps introduce diversity and explore more possible combinations of mnemonic phrases.
Suppose a database population of 100 million randomly generated seed phrases, combined from the words in the BIP-39 dictionary, is created on the server. The program needs to find a sequence of words that unlocks access to a hack bitcoin wallet seed phrase with a positive balance. At the first stage of the calculation, each phrase from this database will be evaluated according to the specified criterion: namely, the balance of the wallet to which the combination of 12 words provides access. The possible values of the wallet balance can only be “positive” or “zero”. Then the algorithm selects the “best” mnemonic phrases with positive balances for crossing. For example, let’s take two best seed phrases and cross them, exchanging parts of the genotypes.
After crossing, the mutation operation occurs, where some genes in the new genotypes are randomly changed. For example, one of the seed phrases may randomly replace one random word with another. Thus, the program creates a new generation of mnemonic phrases, which are evaluated by AI algorithms based on the balance of the wallet. The best mnemonic phrases are passed on to the next generation, and the process is repeated. The starting point of the program module since its launch is the validation of a set of fresh seed phrase populations selected by a genetic algorithm for testing the new population of mnemonic phrases.