Introduction and History of GAs for Permutation Representation

Introduction and history of GAs for permutation representation

1. Combinatorial Optimization

2. Representation – constraints of order and lack of continuity of the search space.

3. GAs and Crossover and Mutation operators

4. Tabu and ACO and more LS

5. EDAs – direct and indirectly, EHBSA, NHBSA

6. More of the problem specific solvers

Motivation

1. GA, schema theorem BB Hypothesis and POP principle

2. Negative Knowledge and negative schema

3. Multi-Objective

   1. Similarity in good candidates – Bad candidates also have similarity BBB Hypothesis – MOMGAII owner arise the term BBB

   2. Conflicts in good candidates

   3. BBs found in both good and bad candidates

4. Permutation Representation

   1. Conflict of constraint order – eg. One number cannot be in more than one position

   2. Similarity and order schema

   3. Chain and why chain

   4. Negative order schema

5. Design and Analysis

   1. PBIL and Incremental learning

   2. Michelsky’s works and more

   3. iBOA

   4. adjacency matrix from EHBSA and MCMC

6. Why the detrimental learning is good for MOPs

Experiments

1. Application

2. Experiment setting parameters/how many run???? encoding

3. Analysis

   1. Structure in conflicts

   2. Objective in conflicts

Conclusion

1 Problem to solve

Representation and dimension

Exploration and premature exploitation

Fitness landscape

Crossover and order schema

Conflicts of building blocks in multimodal and multi objective

Level of confliction

2 limitations

Conflicts of building blocks