Presentation of our paper in Evostar 2016. http://link.springer.com/chapter/10.1007%2F978-3-319-31153-1_8

1. Addressing high dimensional multi-
objective optimization problems by
coevolutionary islands with
overlapping search spaces
Pablo García-Sánchez, Julio Ortega,
Jesús González, Pedro A. Castillo and Juan J. Merelo
pablogarcia@ugr.es

2. Hypothesis
• Using overlapped sections of the chromosome
in a island-based co-MOEA can improve the
quality of the solutions in the same amount of
time.

3. Our approach
• Coevolutionary algorithm MOEA
• Dorronsoro et al. (2013)
• Kimovski et al. (2015): distribution and
combination
• From 8 to 128
• Overlapping sections

4. Disjoint crossover and
mutation
1
1
1
4
4
4
Island #1
Island #4
Example:
8 islands→8 chunks

5. Overlapped crossover and
mutation
1 +1 -1
1 +1 -1
1 +1 -1
-1 4 +1
-1 4 +1
-1 4 +1
Island #1
Island #4
Example:
8 islands→8 chunks

6. Experimental setting
• Problems: ZDT1, ZDT2, ZDT3, ZDT6
• Number of islands: 8, 32, 128
• Chromosome size: 512 and 2048
• Execution time: 25 and 100 seconds
• Global population size: 1024
• Metrics: HV, IGD, Spread

7. Now… numbers!

8. More numbers and tables!

9. Results
• Number of island increases → quality decreases
(but better than baseline and disjoint)
• Number of island increases → overlapping
method is better → find the optimal value
• Time reduction in crossover and mutation →
higher number of generations and modifications
of the PFs

10. Conclusions and future work
• Dividing the chromosome improves all metrics
• Future: different overlapping sizes and real
problems
• Download from https://github.com/hpmoon/
hpmoon-islands

11. Thank you!
• Twitter: @fergunet
• pablogarcia@ugr.es