👉 Evolutionary computing is a subset of artificial intelligence that draws inspiration from biological evolution, particularly natural selection and genetic processes. It involves algorithms that simulate the mechanisms of evolution, such as mutation, crossover (recombination), and selection, to solve optimization and search problems. These algorithms start with a population of candidate solutions, each represented as a set of parameters or "individuals." Over successive generations, the algorithm evaluates the fitness of each solution based on a predefined objective function. Solutions with higher fitness are more likely to be selected for reproduction, combining their features through crossover and introducing variations via mutation to create new offspring. This iterative process continues until a satisfactory solution is found or a predefined stopping criterion is met. Evolutionary computing has been applied to a wide range of fields, from engineering design and robotics to machine learning and bioinformatics, demonstrating its versatility and effectiveness in tackling complex problems where traditional methods may struggle.
