Evolutionary algorithms (EAs) are a subset of evolutionary computation in artificial intelligence that uses mechanisms inspired by biological evolution, such as reproduction, mutation, recombination, and selection. These algorithms are used in optimization and problem-solving applications as they can find solutions that other algorithms might miss due to their iterative and evolutionary approach.
How Evolutionary Algorithms work
The working process of evolutionary algorithms involves four main steps: initialization, selection, genetic operators, and termination.
A population of potential solutions to the problem at hand is generated randomly. These solutions, often referred to as individuals, can represent possible paths, configurations, or decision sequences.
This step involves choosing individuals from the current population to form a new generation. This selection is typically based on their fitness, or how well they solve the problem. Usually, individuals with higher fitness have a better chance of being selected, mimicking the "survival of the fittest" principle in natural evolution.
3. Genetic Operators
After selecting the fittest individuals, the algorithm applies genetic operators such as mutation (random changes in some individuals) and crossover (combining parts of two individuals to create one or more offspring). These operations add diversity to the population and enable the exploration of the solution space.
The algorithm repeats the selection and genetic operators steps for several generations or until a stopping condition is met, for example, reaching a maximum number of generations, finding a satisfactory solution, or not observing significant improvement over a number of iterations.
The evolutionary algorithm's output is the best solution found, that is, the individual with the highest fitness. The main advantage of evolutionary algorithms is their ability to navigate large, complex, and unpredictable search spaces to find satisfactory, if not optimal, solutions.
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