A Study on Multimemetic Estimation of Distribution Algorithms

A Study on Multimemetic Estimation of Distribution Algorithms Rafael Nogueras & Carlos Cotta Memes are patterns-based rewriting rules [CA]: • C, A ∈Σ r with Σ={0,1,#}, r∈Ν • ‘#’ represents a wildcard symbol Meme 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#1#0: PPSN 2014 Ljubljana, Slovenia Memes Genes MEME Self-adaptive Search Engine EDA Cycle 1. Pop Sample(p(x)) 2. pop’ Select(pop) 3. p(x) 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

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R. Nogueras, C. Cotta, A Study on Multimemetic Estimation of Distribution Algorithms, Parallel Problem Solving from Nature - PPSN XIII, T. Bartz-Beielstein et al. (eds.), pp. 322-331, Lecture Notes in Computer Science 8672, Springer-Verlag, Berlin Heidelberg, 2014

Transcript of A Study on Multimemetic Estimation of Distribution Algorithms

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