Genetic algorithms are heuristic search and optimization methods that employ ideas inspired by biological evolution, including survival of the fittest, mutation and recombination. Genetic algorithms have been applied to a wide variety of problems, including feature selection and multidimensional scaling. After a brief tutorial and survey of applications, this talk will describe some recent work extending the genetic algorithms approach to the problem of identifying multiple distinct solutions in highly multimodal search spaces.