L14 - Multi-objective (Evolutionary) Algorithms Continued Flashcards

1
Q

What relation do we use to establish the Pareto front?

A

Dominance relation

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2
Q

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

A
  • 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
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3
Q

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

A
  • 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.
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4
Q

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

A
  • 2 points can be mutually non-dominating, leading to ties.
  • Tiebreak resolution
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5
Q

What is niching?

A
  • 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
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6
Q

What are the types of Tiebreak solution?

A
  • Solution spread
  • Niching
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7
Q

What is Solution Spread?

A
  • When there is a tiebreak during dominance based Pareto selection, choose the point in the least densely populated area?
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8
Q

What is an issue with niching?

A
  • Niche Radius is a net parameter we need to set
  • Problem specific parameter
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9
Q

What is Crowding Distance? Why was it created?

A
  • 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
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10
Q

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

A

Elitist -> Combine best solutions across generations

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

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11
Q

What is an issue with Elitist selection?

A
  • Can cause premature convergence in a local optima
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12
Q

What is an issue with non-elitist selection?

A
  • Discards potentially excellent solutions in parent generations.
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13
Q

What are the issues with Many-Objective Optimisation?

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