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Genetic Algorithms (GAs) are population-based probabilistic search and optimization techniques inspired by natural selection and genetics. Introduced by John Holland in 1965 and popularized in 1975, they are widely used for solving NP-hard optimization problems where traditional methods fail.
Working Principle: A GA starts with a randomly initialized population of candidate solutions (chromosomes). Each chromosome is evaluated using a fitness function. The algorithm iteratively applies selection, crossover, and mutation to evolve better solutions until a convergence criterion is met.
Encoding Methods:
Binary Encoding: Chromosomes as bit strings.
Permutation Encoding: Useful for ordering problems (e.g., TSP).
Value Encoding: Real numbers or characters.
Tree Encoding: For evolving programs or expressions.
Fitness Function: Measures how well a chromosome solves the problem. Higher fitness increases the probability of selection for reproduction.
GA Operators:
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