A Study on Multimemetic Estimation of Distribution Algorithms

Rafael Nogueras & Carlos Cotta

Memes are patterns-based rewriting rules [CàA]: •  C, A ∈Σr with Σ={0,1,#}, r∈Ν

•  ‘#’ represents a wildcard symbol

Meme à Unit of imitation

Encoded in computational representations (meme↔gene)

MMA

Focus on meme manipulation &

propagation

Best Fitness Meme Diversity Meme Success Rate

UMDA PBIL

MIMIC COMIT

TRAP-128

HIFF-128

HXOR-128

SAT-128

Let G=00010011, and let a meme be 01#à1#0:

PPSN 2014 Ljubljana, Slovenia

Memes

Genes

MEME

Self-adaptive Search Engine

EDA Cycle 1.  Pop ß Sample(p(x)) 2.  pop’ ß Select(pop) 3.  p(x) ß Update(pop’) EDA learns the joint probability distribution p(x) using the most promising individuals at each generation.

Wilcoxon ranksum

Multimemetic EDAs with elitism are superior to MMAs. Memetic search process is better when no Laplace correction is used in meme modeling.

Investigate other representation of memes. More complex probabilistic graphical models (Bayesian Networks). Decoupled evolutionary model.

EDA

Non-Elitist Elitist Laplace Non-Laplace Laplace Non-Laplace

Three symbols for problem/EDA respectively indicating how the algorithm compares with its (non-)elitist counterpart, with sMMA, and with the algorithm with the highest median for the corresponding problem (which is marked with a star « in this third position). A white/black circle (�/�) for a worse/better median.

sMMA

Focus on meme modeling

MMEDA

LC