Estimation of Distribution Algorithms

  • Martin Pelikan
  • Mark W. Hauschild
  • Fernando G. Lobo

Abstract

Estimation of distribution algorithms (EDAs) guide the search for the optimum by building and sampling explicit probabilistic models of promising candidate solutions. However, EDAs are not only optimization techniques; besides the optimum or its approximation, EDAs provide practitioners with a series of probabilistic models that reveal a lot of information about the problem being solved. This information can in turn be used to design problem-specific neighborhood operators for local search, to bias future runs of EDAs on similar problems, or to create an efficient computational model of the problem. This chapter provides an introduction to EDAs as well as a number of pointers for obtaining more information about this class of algorithms.

ACO

ant colony optimization

ADF

additively decomposable function

amBOA

adaptive variant of mBOA

AMS

anticipated mean shift

APS

aggregation pheromone system

BIC

Bayesian information criterion

BMDA

bivariate marginal distribution algorithm

BOA

Bayesian optimization algorithm

cGA

compact genetic algorithm

dBOA

decision-graph BOA

DEUM

density estimation using Markov random fields algorithm

distribution estimation using Markov random fields

DSMGA

dependency-structure matrix genetic algorithm

dtEDA

dependency-tree EDA

EA

evolutionary algorithm

EBNA

estimation of Bayesian network algorithm

ECGA

extended compact genetic algorithm

ECGP

extended compact genetic programming

EDA

estimation of distribution algorithm

EDP

estimation of distribution programming

EGA

equilibrium genetic algorithm

EGNA

estimation of Gaussian networks algorithm

EHBSA

edge histogram based sampling algorithm

EHM

edge histogram matrix

EMNA

estimation of multivariate normal algorithm

EvoStar

Main European Events on Evolutionary Computation

EvoWorkshops

European Workshops on Applications of Evolutionary Computation

FDA

factorized distribution algorithm

GECCO

Genetic and Evolutionary Computation Conference

GMPE

grammar model-based program evolution

H-PIPE

hierarchical probabilistic incremental program evolution

hBOA

hierarchical BOA

HCwL

hill climbing with learning

iBOA

incremental Bayesian optimization algorithm

ICE

induced chromosome element exchanger

IDEA

iterated density estimation evolutionary algorithm

IEEE

Institute of Electrical and Electronics Engineers

IUMDA

incremental univariate marginal distribution algorithm

LFDA

learning FDA

mBOA

mixed Bayesian optimization algorithm

MDL

minimum description length

MIMIC

mutual information maximizing input clustering

MMEA

model-based multiobjective evolutionary algorithm

MN-EDA

Markov network EDA

MOSES

meta-optimizing semantic evolutionary search

MPM

marginal product model

NHBSA

node histogram based sampling algorithm

PBIL

population-based incremental learning

PEEL

program evolution with explicit learning

PIPE

probabilistic incremental program evolution

PMBGA

probabilistic model-building genetic algorithm

PPSN

parallel problem solving in nature

PRODIGY

program distribution estimation with grammar model

QAP

quadratic assignment problem

rBOA

real-coded BOA

RM-MEDA

regularity model based multiobjective EDA

SEAL

simulated evolution and learning

SG-GP

stochastic grammar-based genetic programming

SHCLVND

stochastic hill climbing with learning by vectors of normal distribution

UMDA

univariate marginal distribution algorithm

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Martin Pelikan
    • 1
  • Mark W. Hauschild
    • 2
  • Fernando G. Lobo
    • 3
  1. 1.SunnyvaleUSA
  2. 2.Dep. Mathematics and Computer ScienceUniversity of Missouri–St. LouisSt. LouisUSA
  3. 3.Dep. Engenharia Electrónica e InformáticaUniversidade do AlgarveFaroPortugal

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