L14 - Multi-objective (Evolutionary) Algorithms Continued

Question 1

What relation do we use to establish the Pareto front?

Answer 1

Dominance relation

Question 2

How can we use tournament selection with dominance criterion for multiple objectives?

Answer 2

• Choose points X and Y and check for dominance
• If yes, no poblem
• If no, we choose reference set of points Z1…ZN
• Check how many Z’s X and Y dominate
• Select the X or Y that dominates the most Z’s

Question 3

In selection of multiple objectives using dominance criterion, what is C? What’s its impact?

Answer 3

• C is the number of Z’s that are selected for reference for X and Y
• Greater C = Larger scope of Z’s and thus more chance of X or Y dominating the other.

Question 4

What is the issue with tournament based Pareto front selection? What is a solution to this?

Answer 4

• 2 points can be mutually non-dominating, leading to ties.
• Tiebreak resolution

Question 5

What is niching?

Answer 5

• A solution to tiebreak in dominance base Pareto selection
• Create a selection contained in a niche radius around each solution wen might keep.
• Niche radius is a parameter we choose
• Prefer niches with few solutions

Question 6

What are the types of Tiebreak solution?

Answer 6

• Solution spread
• Niching

Question 7

What is Solution Spread?

Answer 7

• When there is a tiebreak during dominance based Pareto selection, choose the point in the least densely populated area?

Question 8

What is an issue with niching?

Answer 8

• Niche Radius is a net parameter we need to set
• Problem specific parameter

Question 9

What is Crowding Distance? Why was it created?

Answer 9

• A distance measure that removed the need for the niche radius
• Compute the distance between each solution and its nearest neighbour and great a square around each point on the Pareto front
• Choose the most sparsely populated square

Question 10

What is the difference between an Elitist and Non-Elitist selection?

Answer 10

Elitist -> Combine best solutions across generations

Non-elitist -> Only have best solutions from most recent generation

Question 11

What is an issue with Elitist selection?

Answer 11

• Can cause premature convergence in a local optima

Question 12

What is an issue with non-elitist selection?

Answer 12

• Discards potentially excellent solutions in parent generations.

Question 13

What are the issues with Many-Objective Optimisation?

Answer 13

• Visualisation issues with a high dimension Pareto front
• Reduction in search capability
• Exponential increase in the number of solutions needed for a Pareto front