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A Consensus Mechanism Based on an Improved Genetic Algorithm

DOI: 10.4236/oalib.1106713, PP. 1-6

Subject Areas: Bioinformatics

Keywords: Consensus Mechanism, Genetic Algorithm, Improvement

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Abstract

An important feature of blockchain technology is that all participants jointly maintain transaction data and can achieve mutual trust relationships without integrated control, which relies on distributed consensus algorithms. Practical Byzantine Fault Tolerant algorithm (PBFT) is a fault-tolerant algorithm based on state machine replication, which solves the Byzantine error, that is, the malicious behavior of nodes. In PBFT, all participating nodes are divided into the primary node and backup nodes. When this primary node commits evil or fails, it will elect a primary node again for message communication. The genetic algorithm (GA) is a computer simulation study inspired by the natural biological genetic evolution criterion “natural selection, survival of the fittest”. Genetic algorithm is actually a method to find the optimal solution. According to it, the best primary node is selected in the PBFT algorithm to improve consensus efficiency. The consensus algorithm is the guarantee of the decentralization feature in blockchain technology. The PBFT algorithm is a commonly used consensus algorithm. However, this algorithm has the following problems: when the primary node fails, it must be selected again, which leads to a decrease in consensus efficiency. This paper proposes a consensus mechanism based on an improved genetic algorithm, which uses an improved genetic algorithm to select the primary node. According to the genetic algorithm, the best primary node is selected, and it meets the minimum number of errors or evils and the highest transaction efficiency with other backup nodes. The improved consensus algorithm can effectively reduce system delay and improve consensus efficiency.

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Yang, C. , Wang, T. and Wang, K. (2020). A Consensus Mechanism Based on an Improved Genetic Algorithm. Open Access Library Journal, 7, e6713. doi: http://dx.doi.org/10.4236/oalib.1106713.

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