Cec09

Lourdes Araujo UNED Juan J. Merelo Guervós Univ. de Granada MULTIKULTI ALGORITHM: USING GENOTYPIC DIFFERENCES IN ADAPTIVE DISTRIBUTED EVOLUTIONARY ALGORITHM MIGRATION POLICIES

Motivation

Island model improves sequential exec.
Asynchronous models better
Asynchronous populations better

 Diversity advantages

Hypothesis

Exchanging best individuals is not always the best

Population 1 Population 2

Hypothesis ? ? ? ? ? ?

Hypothesis

Criteria to select migrant individuals

The most different to the receiving population.
The most different to the receiving population taking from an elite

Which is the most different to the whole destination population? Representing the population:

The best

Consensus sequence

Consensus sequence Composed of the most frequent allele for each gen. 1 0 1 1 1 0 0 1 1 0 0 0 1 0 1 1 0 0 1 1 1 0 0 1 0 1 1 0 0 0 1 0 1 1 0 0 1 1 1 1 0 0 0 0 1 0 1 0 1 1 1 CS: 0 0 1 1 1 0 0 0 1 0 0 0 1 0 1 1 1

Our model Genotype representation G2 “ rather” different to G2 G1 G3 “ rather” different to G3 P3 P1 P2 Pn

Implementation

Chromosomes: fixed length binary strings
Crossover: two points and GBX (respects gene boundaries)
Mutation: single-bit-flip
Perl implementation
Simulated parallel environment

Tested problems P-Peaks: P=100, N=64, 100, 128

Tested problems fitness si (0) = 1.0 fitness si (1) = 0.0 fitness si (2) = 0.360384 fitness si (3) = 0.640576 fitness si (4) = 0.360384 fitness si (5) = 0.0 fitness si (6) = 1.0 MMDP: K=15, 20

Results: P-Peaks (N=64, 2 nodes)‏

Results: P-Peaks(N=64, 4 nodes)

Results: P-Peaks(N=64, 8 nodes)

Results: MMDP(90 bits, 2pX, 2 nodes)

Results: MMDP(90 bits, 2pX, 4 nodes)

Results: MMDP(120 bits,GBX, 2 nodes)

Entropy:

Entropy: averages

Future works Conclusions

Diversity improves the results provided
the individual is good enough
Entropy tests reveal a real increase in
diversity
Both, the best individual and the consensus sequence are good representatives of the population

Conclusions