Reading

• Haupt and Haput Chapter 1 (cursory)
  • we will not dwell on the various optimisation algorithms discussed here
  • the general background is important though
• Haupt and Haput Chapter 2
  • The main point here is the binary GA, which is summarised as Matlab code at the end of the chapter
  • There are many variants of each constituent step, including cross-over, matchmaking, and mutation

Briefing

• Optimisation is a general problem
  • intelligent agents optimise actions
  • functions are optimised in all kinds of decision making
• Two classic approaches
  • exhaustive approaches - exploritng the entire solution space is rarely tractible
  • iteratively improving a tentative solution is easily stuck in local maxima
• Population based methods
  • multiple tentative solutions scattered over the search space
  • many variants
• Most basic - look up R&N
• Genetic algorithms

Exercises