Evolutionary Algorithms
Optimization algorithms inspired by natural evolution that use selection, mutation, and reproduction to find high-quality solutions to complex problems.
What are Evolutionary Algorithms?
Evolutionary algorithms begin with a population of candidate solutions to a problem. Each solution is evaluated according to a defined objective or fitness function. Better-performing candidates are selected and modified through operations such as mutation and crossover to create new generations. Over multiple iterations, the population can evolve toward increasingly effective solutions.
Why are Evolutionary Algorithms Important?
Some optimization problems are too complex for traditional methods or contain large, irregular search spaces. Evolutionary algorithms can explore many possible solutions without requiring detailed assumptions about the problem. This makes them useful for discovering effective solutions to difficult optimization and search tasks.
Common use cases
Evolutionary algorithms are commonly used in engineering optimization, robotics, scheduling, feature selection, neural architecture search, game AI, and automated system design.