Mini toolsDefinition
Optimization algorithms inspired by natural selection that evolve solutions through mutation and crossover operations.
Detailed Explanation
Genetic algorithms maintain a population of potential solutions and iteratively improve them through selection, crossover, and mutation operations. Solutions are evaluated using a fitness function, and better solutions are more likely to be selected for producing the next generation. This process continues until a satisfactory solution is found or resources are exhausted.
Use Cases
Parameter optimization, Feature selection, Network architecture search
