Genetic algorithms (GAs) are algorithms inspired by the process of natural selection and evolution, used to find approximate solutions to complex problems by evolving a population of candidate solutions over multiple generations. Genetic algorithms are particularly useful when searching for solutions in large and complex solution spaces. The representation of the problem is crucial.
Steps:
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Initialize: Begin by creating an initial population of candidate solutions (often referred to as "chromosomes"). Each chromosome represents a potential solution to the problem.
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Evaluate: A fitness function measures how well each solution solves the problem. It quantifies the quality of each solution in terms of the problem's objectives.
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Select: Choose chromosomes based on fitness to act as parents for the next generation.
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Crossover (Recombination): Pair selected parent chromosomes, and perform crossover to create new child chromosomes.
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Mutate: Apply mutation operators to some of the child chromosomes. Mutation introduces small, random changes to individual chromosomes, to introduce diversity and prevent premature convergence.
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Replacement: Create a new population of chromosomes for the next generation. This may involve replacing some or all of the current population with the offspring created through crossover and mutation.
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Repeat for multiple generations, until a termination condition is met.
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End: Extract the best solution from the final population.