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title: Optimisation and GA
categories: session
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# Reading
The textbook chapters are available at BlackBoard.
+ 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
