1 / 7

Elitist Non-dominated Sorting Genetic Algorithm: NSGA-II

Elitist Non-dominated Sorting Genetic Algorithm: NSGA-II. Tushar Goel ( Kalyanmoy Deb). One of most popular MOGA algorithms. Used in Matlab’s gamultobj. Min f 2. Min f 1. Pareto optimal front. Usual approaches: weighted sum strategy, multiple- constraints modeling

zamir
Download Presentation

Elitist Non-dominated Sorting Genetic Algorithm: NSGA-II

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Elitist Non-dominated Sorting Genetic Algorithm: NSGA-II TusharGoel (Kalyanmoy Deb) One of most popular MOGA algorithms. Used in Matlab’sgamultobj

  2. Min f2 Min f1 Pareto optimal front • Usual approaches: weighted sum strategy, multiple-constraints modeling • Alternative: Multi-objective GA • Algorithm requirements: • Convergence • Spread

  3. f2 f1 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.

  4. f2 f1 Parent Child Elitism • Elitism: Keep the best individuals from the parent and child population

  5. f2 f1 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 a b

  6. Flowchart of NSGA-II Begin: initialize population (size N) Evaluate objective functions Rank population Child population created Selection Crossover Report final population and Stop Mutation No Evaluate objective function Stopping criteria met? Elitism Combine parent and child populations, rank population Yes Select N individuals

  7. 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.

More Related