Genetic and deep learning clusters based on neural networks for management decision structures

Judgments are taken in a structured way; both human and business management decisions involve a hierarchical process that requires a level of compromise between risk, cost, reward, experience and knowledge. This article proposes a management decision structure that emulates the human brain approach based on genetic and deep learning cluster algorithms and the random neural network. Reinforcement learning takes quick and specific local decisions, deep learning clusters enables identity and memory, and deep learning management clusters make final strategic decisions. The presented genetic algorithm transmits the learned information to future generations in the network weights rather than the neurons. Because the subject's information, a combination of memory, identity and decision data, is never lost but transmitted, the genetic algorithm provides immortality. The management decision structure has been applied and validated in a smart investment Fintech application: an intelligent banker that makes buy and sell asset decisions with an associated market and risk that entirely transmits itself to a future generation. Results are rewarding; the management decision structure with genetics and machine learning based on the random neural network algorithm that emulates the human brain and biology transmits information to future generations and learns autonomously, gradually and continuously while adapting to the environment.