1. Introducing Mixtures of Generalized Mallows in Estimation of Distribution Algorithms Josian Santamaria Josu Ceberio Roberto Santana Alexander Mendiburu Jose A. Lozano X Congreso Español de Metaheurísticas, Algoritmos Evolutivos y Bioinspirados - MAEB2015

2. Outline Background The Mallows and Generalized Mallows models Mixtures of Generalized Mallows models Experimentation Conclusions and future work 2

3. Estimation of distribution algorithms Definition 3

4. 4 Despite their success, poor performance on permutation problems.

5. Permutation optimization problems Definition Combinatorial problems whose solutions are naturally represented as permutations 5

6. Permutation optimization problems Notation 6 A permutation is a bijection of the set onto itself,

7. Permutation optimization problems Goal To find the permutation solution that minimizes a fitness function The search space consists of solutions. 7

8. Permutation optimization problems Travelling salesman problem (TSP) Permutation Flowshop Scheduling Problem (PFSP) Linear Ordering Problem (LOP) Quadratic Assignment Problem (QAP) 8

9. Permutation optimization problems Travelling salesman problem (TSP) Permutation Flowshop Scheduling Problem (PFSP) Linear Ordering Problem (LOP) Quadratic Assignment Problem (QAP) 9

10. Permutation Flowshop Scheduling Problem Definition Total flow time (TFT) m1m1 m2m2 m3m3 m4m4 j4j4 j1j1 j3j3 j2j2 j5j5 jobs machines processing times 5 x 4 10

11. Why poor performance? The mutual exclusivity constraints associated with permutations Our proposal: probability models for permutation spaces Estimation of Distribution Algorithms Definition 11  Mallows  Generalized Mallows  Plackett-Luce

12. The Mallows model Definition A distance-based exponential probability model Central permutation Spread parameter A distance on permutations 12

13. The Mallows model Definition A distance-based exponential probability model Central permutation Spread parameter A distance on permutations 13

14. The Mallows model Definition A distance-based exponential probability model Central permutation Spread parameter A distance on permutations 14

15. The Generalized Mallows model Definition If the distance can be decomposed as sum of terms then, the Mallows model can be generalized as The Generalized Mallows model n-1 spread parameters 15

16. The Generalized Mallows model Kendall’s-τ distance 16 Kendall’s-τ distance: calculates the number of pairwise disagreements. 1-2 1-3 1-4 1-5 2-3 2-4 2-5 3-4 3-5 4-5

17. Learning in 2 steps: Calculate the central permutation Maximum likelihood estimation of the spread parameters. Sampling in 2 steps: Sample a vector from Build a permutation from the vector and The Generalized Mallows model Learning and sampling 17

18. Drawbacks 18 The Generalized Mallows is an unimodal model, and may not detect the different modalities in heterogeneous populations.

19. Mixtures of Generalized Mallows models 19

20. Mixtures of Generalized Mallows models Learning 20 Given a data set of permutations, we calculate the maximum likelihood parameters from Expectation Maximization (EM)

21. Mixtures of Generalized Mallows models Expectation Maximization (EM) 21 Initialize the weights to Initialize randomly the models in the mixture E step Estimate the membership weight of to the cluster M step Compute the weights as Compute the parameters of the models with

22. Mixtures of Generalized Mallows models Sampling 22 Stochastic Universal Sampling

23. Mixtures of Generalized Mallows models Sampling 23 Stochastic Universal Sampling

24. Problems:  Permutation Flowshop Scheduling Problem (10 instances)  Quadratic Assignment Problem (10 instances) Experiments Settings 24

25. 25 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 The quadratic assignment problem (QAP)

26. Elementary Landscape Decomposition The quadratic assignment problem (QAP) The quadratic assignment problem (QAP) 26 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8

27. Problems:  Permutation Flowshop Scheduling Problem (10 instances)  Quadratic Assignment Problem (10 instances) Algorithms: Generalized Mallows EDA – Kendall’s-tau Mixtures of Generalized Mallows EDA – Kendall’s-tau Generalized Mallows EDA – Cayley Mixtures of Generalized Mallows EDA – Cayley Experiments Settings 27

28. Other distances Cayley distance Calculates the minimum number of swap operations to convert in. 28

29. Problems:  Permutation Flowshop Scheduling Problem (10 instances)  Quadratic Assignment Problem (10 instances) Algorithms: Generalized Mallows EDA – Kendall’s-tau Mixtures of Generalized Mallows EDA – Kendall’s-tau Generalized Mallows EDA – Cayley Mixtures of Generalized Mallows EDA – Cayley Two models in the mixture, G=2 Average Relative Percentage Deviation (ARPD) of 20 repetitions Stopping criterion: 100n-1 generations Experiments Settings 29

30. Extension of the toolbox MATEDA for the mathematical computing environment Matlab Experiments Settings 30

31. Experimentation Results 31 InstanceGM ken Mix ken GM cay Mix cay QAP n10.1 0.3130.5710.1660.022 n10.2 0.0290.0380.0160.006 n10.3 0.1940.2760.0980.021 n10.4 0.0600.0660.0320.019 n10.5 0.2400.3310.1480.063 n20.1 0.9161.2541.0580.548 n20.2 0.0520.0720.0630.031 n20.3 0.8490.9260.9110.576 n20.4 0.0710.0770.0750.050 n20.5 0.5080.6790.6910.419

32. Experimentation Results 32 InstanceGM ken Mix ken GM cay Mix cay PFSP n10.1 0.0050.0080.0030.004 n10.2 0.0050.0080.000 n10.3 0.0200.0170.0120.005 n10.4 0.0070.0100.0010.000 n10.5 0.0060.0100.0020.001 n20.1 0.0220.0320.0780.026 n20.2 0.0280.0310.0820.034 n20.3 0.0250.0360.0840.032 n20.4 0.0210.0320.0860.023 n20.5 0.0230.0290.0680.027

33. Results summary 33 Generalized Mallows EDA Generalized Mallows EDA Kendall’s-PFSPKendall’s-QAP

34. Results summary 34 Generalized Mallows EDA Generalized Mallows EDA Kendall’s-PFSPKendall’s-QAP Mixtures of Generalized Mallows EDA Cayley-PFSPCayley-QAP

35. Conclusions 35 Promising results of mixtures models.

36. Future work 36 Investigate the reason for which the distances behave differently.

37. Future work 37 Evaluate the performance of mixtures with more components (G>2) and implement methods that tune the parameter G automatically.

38. Future work 38 Extend the experimentation to larger instances and more problems

39. Introducing Mixtures of Generalized Mallows in Estimation of Distribution Algorithms Josian Santamaria Josu Ceberio Roberto Santana Alexander Mendiburu Jose A. Lozano X Congreso Español de Metaheurísticas, Algoritmos Evolutivos y Bioinspirados - MAEB2015