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Abstract Artificial intelligence is the term used to describe systems or machines that mimic human intellect in order to carry out tasks that allow them to become better over time based on the data they gather. Artificial intelligence can take many different forms. Artificial intelligence is less about a particular form or function and more about the capacity for thought and data analysis. As a branch of artificial intelligence, neural networks also referred to as neurons or nodes are created by manipulating weights and watching them with the help of experimental data and experience. In an industry where knowledge is acquired by training and stored in neurons through forces known as synaptic weights, it is comparable to the human brain. Additionally, there is a dynamic ring that allows scientists to utilise these networks in order to understand how biological phenomena evolve. Genetic programming (GP), a different artificial intelligence model, is a method of making programmes appropriate for a given task by applying operations to the population of programmes that are equivalent to natural genetic processes. The population of programmes is initially made up of unfit (sometimes random) programmes. The procedures involve choosing the most suitable programmes for replication, crossover, and mutation based on a predetermined fitness metric, which is typically skill at the intended activity. The crossover operation creates fresh and distinct children that are incorporated into the next generation of programmes by switching specific components of chosen couples (parents). Certain programmes that are not chosen for replication are replicated from the present generation to the following one. A mutation occurs when one random programme component is changed for another random component. Subsequently, the fresh generation of programmes undergo recursive application of the selection and other processes . |