Elitist Non-dominated Sorting Genetic Algorithm: NSGA-II Tushar
Goel (Kalyanmoy Deb) One of most popular MOGA algorithms. Used in
Matlabs gamultobj
Slide 3
[email protected] 2 Pareto optimal front Usual approaches:
weighted sum strategy, multiple-constraints modeling Alternative:
Multi- objective GA Algorithm requirements: Convergence Spread Min
f 2 Min f 1
Slide 4
[email protected] 3 Ranking Children and parents are combined.
Non-dominated points belong to first rank. The non-dominated
solutions from the remainder are in second rank, and so on. f2f2
f1f1
Slide 5
[email protected] 4 Elitism Elitism: Keep the best individuals
from the parent and child population f2f2 f1f1 Parent Child
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[email protected] 5 Niching for last rank Niching is an operator
that gives preference to solutions that are not crowded Crowding
distance c = a + b Solutions from last rank are selected based on
niching f2f2 f1f1 a b
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[email protected] 6 Flowchart of NSGA-II Begin: initialize
population (size N) Evaluate objective functions Selection
Crossover Mutation Evaluate objective function Stopping criteria
met? Yes No Child population created Rank population Combine parent
and child populations, rank population Select N individuals Elitism
Report final population and Stop
Slide 8
Problems NSGA-II Sort all the individuals in slide 4 into
ranks, and denote the rank on the figure in the slide next to the
individual. Describe how the 10 individuals were selected, and
check if any individuals were selected based on crowding distance.
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