1. Recent Research about LTGA Lecturer:Yu-Fan Tung, R02921044 Advisor:Tian-Li Yu Date:May 8, 2014

2. Outline  Introduction  Background Knowledge  Overview of Recent Research  Forced Improvement (FI)  Family of Subsets (FOS)  Linkage Neighbors (LN)  Multiscale Linkage Neighbors (MLN)  LTNGA  Conclusion

3. Introduction  LTGA is a really new branch of GA.  The Linkage Tree Genetic Algorithm, Dirk Thierens, 2010

4. Background Knowledge  LTGA  There is a model-building phase between selection and crossover.  Crossover based on the masks identified in the model- building phase.  “Mask”: Only crossover part of the chromosome, mask the rest

5. Background Knowledge  LTGA  In the model building phase, first join two nearest clusters until there are exactly two left. (Mutual Information)

6. Background Knowledge

10.  The advantage of OM: 1.Chromosomes are mixed and replaced toward higher fitness, which is like a less noisy selection 2.Optimal Mixing requires relatively small population size compared to most GA.

11. Overview of Recent Research  Predetermined versus Learned Linkage Models, Thierens, 2012.  Incremental Gaussian Model-Building in Multi-Objective EDAs with an Application to Deformable Image Registration, Bosman, 2012.  Linkage Neighbors, Optimal Mixing and Forced Improvements in Genetic Algorithms, Bosman, 2012.  More Concise and Robust Linkage Learning by Filtering and Combining Linkage Hierarchies, Bosman, 2013.  Hierarchical Problem Solving with the Linkage Tree Genetic Algorithm, Thierens, 2013.  On the usefulness of linkage processing for solving MAX-SAT, Sadowski, 2013.  Solving Satisfiability in Fuzzy Logics by Mixing CMA-ES, Brys, 2013.

12. Forced Improvement  Forced improvement (FI)  In OMEA, tournament selection of tournament size 2 is performed, but OM acts like selection  This reduces diversity faster than we want.  No selection leads to better performance.

13. Forced Improvement

14.  Problem: Without selection, GA will not halt easily. Real world problems often have many local optimals.  Niching technique is necessary.  For a receiver R, it is possible that it is not improved after one run of OM.  If so, an extra run of OM is initiated, with the best chromosome in population as donor D.  The extra run of OM stops immediately if improvement is made.

15. Forced Improvement  Advantage: 1.Will not affect selection pressure too much, since the extra OM doesn’t happen frequently. 2.Puts a very specific selection pressure on the convergence phase toward the best solution.

16. Family of Subsets

17. Linkage Neighbors  LTGA cannot represent overlapping BB well.

18. Linkage Neighbors  Motivation:  Consider 0 1 2 3  In LT, they can only be partitioned as [012][3] or [0][123].  In LN, this can be represented as [012][1203][2103][312].  The order of variables in masks in LN is shuffled to eliminate bias.

19. Linkage Neighbors

23.  Advantages of using LN: 1.It outperforms LT in most case. 2.To build LN is at least as efficient as LT. 3.It can be parallelized in a straightforward way.  Disadvantage:  The “threshold” method seems to simple.

24. MLNGA  Multiscale LNGA.  LN: [012] [1203] [2103] [312]  MLN: [02][012] [1203] [23][213][2103] [3][312]  Four methods are proposed to build MLN from LN. 1.Linear Decomposition 2.Exponential Decomposition 3.Bucket-linear Decomposition 4.Bucket-exponential Decomposition  The main idea is to remove weakly linked variables and preserve linkage with higher MI. Use bucket sort for performance issue.

25. MLNGA

27.  Advantage:  Representing linkages at multiple scales can be beneficial.  Disadvantage:  Too many redundant masks.

28. LTNGA  A combination of LTGA and MLNGA.  Learn a LTGA model, learn a MLNGA model, and take the union.  Filtered version. For, remove LS is defined as the average MI of all pairs in the set.

29. Conclusion  The development of LTGA is fast.  LTGA, 2010.  ROMEA, GOMEA, 2011.  LNGA, MLNGA, 2012.  LTNGA, 2013.  Effectively mixing is important.

30. Reference  Predetermined versus Learned Linkage Models, Thierens, 2012.  Linkage Neighbors, Optimal Mixing and Forced Improvements in Genetic Algorithms, Bosman, 2012.  More Concise and Robust Linkage Learning by Filtering and Combining Linkage Hierarchies, Bosman, 2013.  Hierarchical Problem Solving with the Linkage Tree Genetic Algorithm, Thierens, 2013.

31. Thanks for attention Any question or suggestion is welcomed.