GAs are useful for hypothesis spaces that are

• complex
• not well understood
• unknown interactions between hypothesis "parts"
• for which learning methods have not been developed
• not smooth
• unpredictable and difficult, e.g.,
  • optimization
  • template rules
  • computer programs