(Intro To) Evolutionary Computation
Revision Lecture
Ata Kaban
The University of Birmingham
Overview
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Overview of key notions and techniques
Example questions
Revise worked problem solutions
Taking questions
Representation
• Deciding on the representation is the first step in
designing an EA application
• We had examples of
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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?
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?
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?
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
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 Prisoner’s Dilemma
– Combinatorial optimisation: Travelling
Salesman Problem
– Classifier systems & evolving NN – e.g. could
you devise a solution to weather prediction?
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, pseudocode, formulas or whatever is more comfortable for
you to express your answer.
Don't forget to revise the last few lectures'
topics either!
- Estimation of Distributions Algorithms
(EDA)
- Theory of EA
Some more advices
• Make sure you know where the exam takes place
• Even if you don’t 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
– Don’t 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
Good Luck!