Genetic Algorithms
Genetic Algorithms (GA) use the analogy of natural evolution of species: natural selection and reproduction guide the evolution towards individuals that are better adapted to the environment. In this way GA lead to optimal designs.
MOGA-II is an improved version of Multiobjective Genetic Algorithm (MOGA). It uses a smart multisearch elitism for robustness and directional crossover for fast convergence. Its efficiency is ruled by its reproduction operators: classical crossover, directional crossover, mutation, and selection.
NSGA-II stands for Non-dominated Sorting Genetic Algorithm II, and is a fast and elitist multiobjective evolutionary algorithm. Main features are a parameter-less diversity preservation mechanism and the capability of dealing directly with continuous variables.
ARMOGA stands for Adaptive Range Multiobjective Genetic Algorithm. Range Adaptation can adjust the search region according to the statistics of the former data, and help to reduce the number of function calls.