A genetic algorithm is a search-based optimization technique inspired by natural selection, which is used to find optimal or near-optimal solutions to complex problems. It works by creating an initial population of random potential solutions and iteratively improving them through processes that mimic evolution: selection, crossover, and mutation. These algorithms are widely used in machine learning and other fields to solve problems that are difficult to solve using other methods
How genetic algorithms work
An initial population of random candidate solutions is created. Each solution is represented as a string of binary digits or other data structures, similar to a chromosome.
The “fitness” of each individual solution is evaluated based on how well it solves the problem. This is done using a fitness function, which assigns a score to each solution.
The fittest individuals are selected from the population to become “parents” for the next generation. This mimics “survival of the fittest”.
Crossover:
The genetic material of two selected parents is combined to create new offspring. This process involves swapping parts of the parents’ solutions to create new, hybrid solutions.
Mutation:
Random changes are introduced into the offspring’s genetic material. This helps the algorithm explore new possibilities and avoid getting stuck in local optima.
Iteration:
The process of selection, crossover, and mutation is repeated for several generations. New offspring are created from the fittest parents, and the population gradually evolves towards a better solution.
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