Genetic Algorithms
A search heuristic optimization method inspired by natural biological evolution that iteratively evolves candidate solutions toward optimal physics configurations.
Governing formula
Evolutionary Operators: Selection, Crossover, Mutation
SI unit
Stochastic Heuristic Optimization
In depth
Maintains a population of candidate parameter solutions. Evaluates fitness, selects best performers, and generates new generations via crossover and mutation operators, efficiently exploring rugged high-dimensional search spaces.
Examples in the real world
Optimizing stellarator magnetic confinement coil shapes and designing photonic crystal metamaterials.