Living in the modern world requires having a precise, intelligent system that may be suggested to do various activities. Due to the scalability of AI algorithms, we have proposed a phenotypic Evolutionary Algorithm (EA)-based system to assist the Artificial Neural Network algorithm (ANN) in the learning process. Combining the two strategies can result in a smart neuro- evolutionary model that is effective in accomplishing significant duties in various domains. The suggested multi-layer neural algorithm's design creates the conditions for learning via the EA’s processes of crossover, mutation, and selection. To aid in the selection and crossover processes, the learning process phase breaks up the original ANN into multiple ANNs according to the number of hidden layers. ANNs are ranked from worst to best in the selection phase based on the soring function that is applied to the fitness list. The fitness list retains each ANN’s accuracy even after breaking apart the original ANN. The crossover procedure is then applied between the two worst and best ANNs. Mutation provides a means of improvement for the less effective ANNs. Following completion of these processes, the ANN algorithms are combined to create a single ANN algorithm. The Vicon mobile robot (SCITOS G5) system’s multi-dimensional data, which extracted both aggressive and typical human movement, as well as Human Activity Recognition (HAR) datasets extracted by smartphones, both have been applied using the suggested method. The system achieved a high performance and efficiency rate on the intended recognition problem.

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