Superintelligence: Paths, Dangers, Strategies

by Nick Bostrom

Evolution-based methods, such as genetic algorithms and genetic programming, constitute another approach whose emergence helped end the second AI winter. It made perhaps a smaller academic impact than neural nets but was widely popularized. In evolutionary models, a population of candidate solutions (which can be data structures or programs) is maintained, and new candidate solutions are generated randomly by mutating or recombining variants in the existing population. Periodically, the population is pruned by applying a selection criterion (a fitness function) that allows only the better candidates to survive into the next generation. Iterated over thousands of generations, the average quality of the solutions in the candidate pool gradually increases. When it works, this kind of algorithm can produce efficient solutions to a very wide range of problems—solutions that may be strikingly novel and unintuitive, often looking more like natural structures than anything that a human engineer would design.