(Intro To) Evolutionary Computation Revision Lecture

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(Intro To) Evolutionary ComputationRevision
Lecture

• Ata Kaban
• The University of Birmingham

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Overview

• Overview of key notions and techniques
• Example questions
• Revise worked problem solutions
• Taking questions

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Representation

• Deciding on the representation is the first step
  in designing an EA application
• We had examples of
• Binary
• Real valued
• Trees (GP)
• Special, e.g.
• Order-based in the TSP problem, need to repr
  tours
• Rule-based need to represent sets of rules
• Representation for NNs
• Q Could you decide on a suitable representation
  when given a problem description?

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Genetic Operators

• Depend on the representation
• Mutation-type (one parent)
• Crossover-type (typically two parents)
• Self-adaptation
• Q Can you describe crossover and mutation
  operators for each representation scheme?
• Q Can you describe differences between different
  crossover or mutation operators?
• Q Can you say when, how and why would you use
  self-adaptation?

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Fitness Computation and Selection Schemes

• Selection schemes
• Roulette, tournament, ranking,
• Fitness Sharing, Niching, Crowding
• These are methods to control population diversity
• Q Could you list advantages and disadvantages of
  different selection schemes
• Q Could you explain the differences between
  explicit fitness sharing and implicit fitness
  sharing as well as their advantages and
  disadvantages?

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Other topics

• Co-Evolution
• Competitive or cooperative
• One or several populations
• Constraint Handling
• Penalty approach (static, dynamic, adaptive)
• Repair approach
• Others (by co-evolution, by multi-obj, by
  designing specialised operators that preserve the
  constraints)
• Multi-objective Optimisation
• Pareto-optimal solution

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Revise Example Problems

• We gave loads of examples all over the place in
  the lecture to illustrate notions or techniques.
  We have also worked through detailed solutions to
  some very important to revise them!
• Function optimisation
• Co-evolution Iterated Prisoners Dilemma
• Combinatorial optimisation Travelling Salesman
  Problem
• Classifier systems evolving NN e.g. could you
  devise a solution to weather prediction?

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Types of questions

• A few easy general technical questions
• Specific technical questions
• Problem solving questions given a problem
  description (close to those we had), design an
  appropriate EA solution
• No question requires you to know formulas!
• You can use textual explanation, figures,
  pseudo-code, formulas or whatever is more
  comfortable for you to express your answer.

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• Don't forget to revise the last few lectures'
  topics either!
• - Estimation of Distributions Algorithms (EDA)
• - Theory of EA

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Some more advices

• Make sure you know where the exam takes place
• Even if you dont know the complete answer, write
  as much as you do know.
• We give some points for partial answers also
• Use examples to help you explain things
• Cover as many questions as you can
• Dont spend all your time giving a brilliant
  answer for one question only as there is a
  limited number of points we give for each
  question
• Think a bit before you answer

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Good Luck!