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Evolutionary Computation & Intro to EC – Resit exam 2013
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Part 1: Answer this question
Part 2: Answer 2 out of 3 questions
Part 1
Question 1
a) Is it true that by using a mutation scheme that combines Gaussian and Cauchy mutations
then we can find the global optimum of any function? [5%]
b) Do you see any relation between co-evolution and fitness sharing? [5%]
c) Comment on the analogy of Evolutionary Algorithms (EA) with Darvin’s ideas on biological
evolution. The latter has no specific problem solving scope, so what makes EA suitable for
problem solving? [5%]
d) Which kinds of representation can be used in genetic programming? [5%]
e) Explain the meaning of search bias. [5%]
f) State the No-Free-Lunch theorem. What implication does it have? [5%]
g) Estimation of distribution algorithms (EDA) are a new class of algorithms, which use
neither crossover nor mutation. How is a new generation created in these methods? [5%]
h) What are the main difficulties in designing EDA optimisation algorithms? [5%]
Part 2
Answer 2 out of 3 questions
Question 2
Consider the following very simplified game of poker that is played by 2 people.
The each player is dealt 2 cards, one face up and one face down. Thus, a player can see his/her own 2
cards and one of the cards of the opponent. Each card has a number from 1 to 13, and only the number
is significant. (The suit of the card does not matter.) The value of the player’s hand is the sum of the
numbers of the player’s two cards.
The player has 2 options, bet or fold. The player must choose his/her option before he/she knows what
the opponent has done. If both players fold, then neither player wins anything. If one player bets and the
other folds, the player who bets wins one dollar from the player who folds. If both players bet, then the
player with the higher value hand wins two dollars from the player with the lower value hand. If both
players bet and the values of the hands are the same, then neither player wins anything.
Design an evolutionary algorithm to determine the optimal (or a very good) strategy for playing this
game. The strategy should only depend on the cards seen by the player and not on any previous game.
a) Describe a chromosome representation of individuals, representing strategies for playing this game.
[6%]
b) How many different strategies are there? (How big is the search space?) [6%]
c) Explain how will any of the individuals be used as a strategy. [6%]
d) Discuss alternatives to the representation you gave in a) [6%]
e) What fitness function would you use? [6%]
Question 3
In many real problems in engineering, we need to determine al solutions of some sets of equations (on a
given interval.) Most of these are so complicated that the solutions cannot be obtained analytically and
therefore numerical methods need to be used. A general form of such system of equations is the
following:
f(x)=y
where f is a complicated non-differentiable function, y is a given constant and x is an unknown realvalued vector. The task is to find all x vectors that satisfy this equation. Design an evolutionary
algorithm for solving this task, justifying all your design decisions.
a) Describe a suitable chromosome representation of an individual [6%]
b) Design a suitable fitness function [6%]
c) Describe what evolutionary operators you would use [6%]
d) Describe the stopping criteria to be used [6%]
e) Comment on possible alternative solutions. [6%]
Question 4
a). Coevolutionary learning
i) What is coevolutionary learning and how does it differ from evolutionary learning? [5%]
ii) What types of coevolutionary learning can you distinguish? [5%]
iii) Give an example of intra-population competitive coevolution. [5%]
b) Fitness sharing and niching
i) What is fitness sharing and how is it useful? [5%]
ii) What problems may arise with the method of explicit fitness sharing? [5%]
iii) How is the shared fitness computed in implicit fitness sharing? [5%]