1. (Intro To) Evolutionary Computation Revision Lecture Ata Kaban The University of Birmingham

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

3. 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?

4. 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?

5. 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?

6. 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

7. 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?

8. 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.

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

10. 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

11. Good Luck!