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Heuristic optim ization provides:
z Better solutions: Com pared w ith
num ericalm ethods
z Efficient solutions: Com pared w ith
exhaustive brute force approaches
Techniques and M odels for ParallelGenetic
Algorithm s
z High com plexity problem s:
But:There are som e restrictions on
particular com plex problem s in w hich
traditionalHO techniques are lim ited.
Solutions:
z Very large search spaces
z Fast im plem entations
z Very com plex fitness landscapes
z M ore pow erfulcom puters
z Very large population sizes
z Parallelprocessing:
z Tim e consum ing com putations:
z Due to high com plexity problem s (above)
z Due to costly evaluation functions
z
z Developing new
parallelversions of HO
algorithm s
z
z It w ould provide solution to other difficulties in
som e of these algorithm s (prem ature
convergence in GA).
Parallelim plem entation oftraditionalGA:
z New
paradigm s and algorithm m odels
z GA are specially w ellsuited forparallel
im plem entations (naturalevolution is done
in parallel)
z Very interesting efficiency results.
z
z
GlobalParallelization – there is just a single
population as in serialGAs,but evaluation of
individuals and genetic operators are parallelized.
z Different Parallelization M odels:
z GlobalPopulations:
z
z M aster-Slave M odel
z M ulti-Population:
z
z Island M odel(Coarse Grain)
z
z Cellular M odel(Fine Grain)
z DifferntSynchronization Types:
z Synchronous M aster-Slave:
z
z M aster perform s crossoverand m utation
Master
z
z Slaves perform s fitness calculation
z Sem i-Synchronous M aster-Slave:
z
z Individuals are included w hen fitness
calculation is done.Then slave node takes
anotherone.
Workers
z Asynchronous M aster-Slave:
z
z Shared m em ory,each node perform s also
crossoverand m utation operators.
Behavior of GAs unchanged – single population, random mating
•
z
z
Coarse-grained parallelGA – Population divided into
m ultiple subpopulations,or dem es,that evolve
independently w ith occasionalexchange of
individuals in the form ofm igration.Also called the
island m odel.
z The m ost interesting m odel:
z Each process runs each ow n genetic
algorithm .
z A topology (neighborhood relationship) is
defined to perform individualm igration.
z M igration provides genetic exchange
am ong otherw ise isolated genetic
populations
Introduces fundamental changes in the nature of GAs
z Sm allnum ber of subpopulations -
dem es
z Subpopulations are of large size
z M igration betw een subpopul
ations.
z M ost popular of the PGAs
z Super-linealSpeed-Up
z
Param eters:
Ring
z
z
The topology
z
z M igration frequency
z
z M igration rate
z
z Selected individuals
z
z Replaced individuals
Double
Ring
Isolated
Mesh
Full
Full
1 node
Mesh
Ring
4 nodes
16 nodes
z Experim entations by Pettey et al(1987)
z Best individualin each dem e sent to the
neighbors after every generation
z Island M odelw ith low levelof interaction
w as inefficient
z Island M odelw ith high levelof interaction
w as equivalent to the serialGA.
z
z
Fine-grained parallelGA – Population divided into
large num ber of very sm allsubpopulations,each
subpopulation Evolving on separate processing
elem ent.In the extrem e case,there m ay be just one
individualper processing elem ent.W ellsuited for
m assively parallelcom puters.
Introduces fundamental changes in the nature of GAs
z
z
z
z
z
Large num ber of sm alldem es
Large am ount of interaction.
Individuals in F-PGA are usually placed on a
2-d grid,and com m unication done only w ith
neighbors
O ther topologies can be sim ulated on top of
the grid
ASPARAGO S system introduced by Schleuter
and M ühlenbein is based on F-PGA w hich
also uses a localhillclim bing.