A genetic algorithm (GA) is an optimization technique inspired by the process of natural selection. It mimics biological evolution by using operations such as selection, crossover, and mutation to find solutions to complex problems. In a GA, a population of potential solutions (called individuals) evolves over generations, with each individual represented by a chromosome-like data structure. Solutions are evaluated based on a fitness function, and the fittest individuals are more likely to be selected for reproduction. Through crossover (combining parts of two solutions) and mutation (random alterations), new generations are created, aiming to improve over time. GAs are widely used in fields like engineering, artificial intelligence, and operations research for solving optimization and search problems.
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