Artificial Intelligence Programming 2025 – 400 Free Practice Questions to Pass the Exam

The concept that involves a series of small mutations to improve program performance is known as "Machine Evolution." This idea is closely associated with evolutionary algorithms, which are inspired by the process of natural selection. In this approach, potential solutions to a problem are treated like organisms in a population. Through iterative processes that include selection, mutation, and recombination, these solutions evolve over generations. The intention is to gradually improve the quality of the solutions until they reach a satisfactory level of performance.

Machine Evolution operates on the principle that small, incremental changes can lead to significant enhancements in program efficiency and effectiveness. By applying mutations to the parameters or structure of a program, it fosters exploration of the solution space, allowing algorithms to adapt and improve over time. This concept finds its application in various domains, including optimization problems and adaptive systems within artificial intelligence.

The other concepts listed do not directly relate to the idea of incremental improvements through mutations. For instance, weak methods generally refer to approaches that may not guarantee optimal solutions but can be useful in certain contexts. Microworlds are simplified environments used for specific AI tasks, while the physical symbol system hypothesis relates to the symbolic representation of knowledge and reasoning rather than evolutionary improvement.