Transcript EGC'2005 - Docking@GRID

Laboratoire d’Informatique
Fondamentale de Lille
Parallel Cooperative
Optimization Research
Group
Evolutionary Aglorithms for the
Protein Structure Prediction Problem
and Molecular Docking
A.-A. Tantar, N. Melab and E-G. Talbi
{tantar, melab, talbi}@lifl.fr
INRIA DOLPHIN Project
Supported by the French Research Agency
Outline
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Protein Structure Prediction, Molecular
Docking: modeling and complexity
analysis
Parallel Hybrid Metaheuristics for the PSP
Problem

Grid experimentation on GRID5000

Conclusion and Future Work
Protein Structure Prediction Problem
Amino-acids
Conformational Sampling
...
...
A protein
Native Conformation
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Protein Structure Prediction (PSP) ~ finding the ground-state (tertiary structure)
conformation of a protein, given its amino-acid sequence - the primary structure
Molecular Docking
RECEPTOR
HIV-1 PROTEASE + XK263
HIV-1 PROTEASE
LIGAND
+
XK263 INHIBITOR
Molecular Docking ~ the prediction of the optimal bound conformation of
two molecules exerting geometrical and chemical complementarity.
Scoring Energy Function (A)
Empirical Force Fields – Classical Mechanics

AMBER – Assisted Model Building with Energy Refinement
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CHARMM – Chemistry at HARvard Molecular Mechanics
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OPLS – Optimized Potentials for Liquid Simulations
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GROMOS – GROningen MOlecular Simulation
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SYBYL
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D-Score - DOCK
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F-Score - FlexX
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G-Score - Gold
AutoDock/AutoGrid – suite of toos for automated docking
...
WEAK
London Forces
Van der Waals
dipole-dipole
stacking
hydrogen bond
STRONG
ionic interactions
From recent results Empirical Methods OUTPERFORM Ab Initio Methods (!!)
 Neumaier, A. (1997). Molecular modeling of proteins and mathematical prediction of protein structure.
SIAM Review, 39(3):407 – 460.
Scoring Energy Function (B)
 AutoDock
 DOCK - SYBYL/D-Score
Scoring Energy Function (C)
 GOLD - SYBYL/G-Score
 FlexX - SYBYL/F-Score
 Renxiao Wang. Yipin Lu, and Shaomeng Wang, Comparative Evaluation of 11 Scoring Functions for Molecular
Modeling, J. Med. Chem., 10.1021/jm0203783, 2003, 46, 2287-2303
Scoring Energy Function (D)
the factors model oscillating entities – forces simulated by interconnecting springs between
atoms; offer an easy concept and fast energy evaluations.
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constants derived from higher-level calculations (e.g. Ab initio) – difficult to obtain and to fit.
empirical force fields have the DRAWBACK of offering results not directly comparable with
results obtained through another differently parameterized force field.
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RMSD (Root Mean Squared Deviation) – typical distance measure between conformations.
PSP modeling:
Encoding of the conformations
 Cartesian atomic coordinates
representation
AMINO-ACID
286
165
316
.
.
.
7
69
TORSION-ANGLE BASED
CHROMOZOME
 amino-acid-based encoding –
hydrophobic/hydrophilic models
 all heavy atoms coordinates
 backbone C coordinates
 backbone coordinates and
side-chain centroid coordinates
 torsion-angle based
representation
Molecular Docking:
Encoding of the conformations
Model complexity
High Complexity
 flexible docking - both the
ligand and the receptor are
modeled as flexible molecules;
limitations may be imposed
AutoDock Representation - string of real valued genes
 three Cartesian coordinates for ligand translation
 four variables for defining a quaternion specifying ligand
orientation
 one real value for each ligand torsion
 Garret M. Morris et al., Automated Docking Using a Lamarckian
Genetic Algorithm and an Empirical Binding Free Energy Function,
Journal of Computational Chemistry, Vol. 19, No. 14, 1639-1662, 1998.
 partially flexible docking –
to some extent flexibility is
modeled by focusing on the
smaller molecule or by defining
comprehensive regions of
significance
 rigid docking – extreme
simplification; both the ligand
and the receptor are rigid
entities, no flexibility being
allowed at any point
Low Complexity
+ Multiple Potential Binding Sites
Levinthal’s Paradox
Complexity Analysis
A molecule of 40
residues
10 conformations per
residue
1040 conformations
1014 conformations per second
1028 years
1011 local optima for the [met]-enkephalin
pentapeptide
 75 atoms
 Five amino-acids (Tyr-Gly-Gly-Phe-Met)
 22 variable backbone dihedral angles
Motivation and objectives

PSP and Molecular Docking can be modeled as a
optimization problems …

… which are multi-modal
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… and require a huge amount of resources for large proteins

Need of hybrid metaheuristics
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Need of large scale parallelism (Grid computing)

$$$ : $150000-$250000 per X-ray structure
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Study of two hybrid metaheuristics for PSP:
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Genetic Algorithms and Simulated Annealing
Classification of the Existing Methods
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Energy minimization vs. Geometry complementarity approaches
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Ab initio, de nuovo electronic structure calculations
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Semi-empirical methods
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make use of approximations as substitution for ab initio techniques
employ simplified models for electron-electron interactions
Empirical methods
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rely on quantum mechanics for determining different molecular characteristics
comprise no approximations and no a priori experimental data is required
high computational complexity => reduced size systems
rely upon molecular dynamics (classical mechanics based methods)
often the only applicable methods for large molecular systems, i.e. proteins and polymers
do not dissociate atoms into electrons and nuclei - indivisible entities
Continuous vs. Discretization - Combinatorial Optimization
Classification – Algorithmic Overview (A)
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Global Optimization – Energy Landscape Projections, Convex Global
Underestimation, etc.
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construct a metamodel of the energy surface - avoid most of the inconvenients determined by
the funnel with bumps structure of the landscape
tend to require less computational time than classical techniques – a few orders of magnitude
apparently have a lower probability of remaining blocked in kinetic traps
employ suppositions regarding the landscape which may not extend to different classes
require the construction of a metamodel, underestimation, etc. which offers no
optimality/corectness guarantee
Molecular Dynamics
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offer accurate results as far as the force field model fits the real energy landscape
the method can be applied straightforward – apart the formalism, no parameters to be tuned
require extensive computational resources – may only be applied to reduced models
the design and the development of the formalism may be far from trivial
given the high computational complexity, stand-alone approaches cannot be practical
bond vibration
10-15 femto MD step
isomerization, water dynamics
10-12 pico
10-9 nano,
MD long run
fastest folders
10-6 micro
typical folders
10-3 mili
slow folders
100 seconds
Classification – Algorithmic Overview (B)
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Monte Carlo / Simulated Annealing / Metropolis-based algorihtms
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do not require a large number of parameters – adaptive versions overcome to an extent the
inconvenients determined by the initial specified values
offer a performance guarantee for ataining the optimal value
compared to gradient based methods, are less prone to getting traped in local minima
require a non feasible number of sampling points for having the performance guarantee
appropiate for local search optimization or for the refinement of specific areas
might employ specific distributions for performing the conformational sampling
depending on the nature of the method, it may pose important parallelization problems
Evolutionary algoritmhs – Genetic Algorithms
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exhibit strong exploration characteristics while lacking intensification capabilities –
hybridization approaches tend to be more appropiate
offer the base for complex parallelization techniques
require implementing different components, having different parameters
the design of the operators may require to consider structural aspects (i.e. xover operator
should not mix angles from different amino-acids when performing the recombination, etc.)
do not offer any performance guarantee – statistical convergence
The sketch of an evolutionary algorithm
Best individual
Initialization
Evaluation
Parents
Replacement
Evaluation
Stop ?
Evolutionary
engine
Offspring
Selection
Genitors
Recombination,
mutation
Generation of the Initial population
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Seeding - information inserted from structural databases
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Ab initio approach – no initial structural information is given
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ensure that the population is seeded with the substructures that lead to the optimal
conformation
does not represent the desired approach as strucutural information may not be available in all
the cases
uniform random initialization of the dihedral angles – the most common option
depending on structural data different distributions may be employed for offering a bias
The size of the population
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may have an impact on the convergence rate:
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reduced number of chromosomes -> premature convergence
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larger number of chromosomes -> extra computational time
should be related to the cardinality of the alphabet used for encoding, chromosome length,
algorithm parameters, etc.; empirical, restricted to special cases - no rigorous specifications
 DUSAN P. DJURDJEVIC, MARK J. BIGGS, Ab Initio Protein Fold Prediction Using Evolutionary Aglorithms:
Influence of Design and Control Parameters on Performance, 10.1002/jcc.20440, Wiley InterScience, 2005.
Selection and Replacement (A)
Selection pressure
the probability of selecting the best chromosome as compared to the average selection
probability of all the chromosomes
Selection intensity
population expected average fitness value after performing a selection step having as base a
normalized Gaussian distribution
Selection variance
population expected variance of the fitness distribution after performing a selection step having
as base a normalized Gaussian distribution
Loss of diversity
the percentage of non-selected individuals during the selection phase
Bias
the absolute difference between the expected reproduction probability of a specific
chromozome and the chromosome’s normalized fitness
Spread
range of possible values for the offsprings of a given chromosome
Selection and Replacement (B)
Selection strategy
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fitness-proportionate selection
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rank-based selection
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selection relative to rank and not to the chromosome’s real fitness value
stochastic tournament selection
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no bias, does not guarantee a minimum spread
tournament among randomly chosen individuals
selection pressure can be adjusted by modifying the size of the tournament
offer a constant selection pressure
uniform selection
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selection bias towards sparse fitness levels and not towards best fit chromosomes
might not behave well under extensive genetic diversity conditions (i.e, initial phases)
Selection and Replacement (C)
Replacement strategy
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Global replacement vs. Local replacement
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generational replacement – replace all the parents with the offsprings
uniform replacement - less offsprings than parents, replace at random
elitist replacement - less offsprings than parents, replace worst parents
fitness-based replacement - more offsprings than parents, insert only the best
offsprings
random, first-in-first out – the simplest ones, do not exert strong performace
characteristics
worst-fit replacement – the least fit chromosome(s) are replaced
exponential replacement – chromosomes are ranked from the least fit to the
the most fit, the replacement process employing an associated probability
distribution
...
Recombination Operators
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discrete recombination – exchange of chromosome values; for each locus in the
offspring, the parent chromosomes have equal probabilities of contributing
intermediate recombination – offsprings obtained by interpolation of the parents
Offspring[ loci ] = ui * A[ loci ] + ( 1-ui ) * B [ loci ], ui [ -, 1+ ], i 1,n

line recombination – particular case of intermediat recombination, a single
uniformly random generated variable being considered
Offspring[ loci ] = u * A[ loci ] + ( 1-u ) * B [ loci ], u [ -, 1+ ], i 1,n
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one/multi point crossover – implies the generation of one/multiple cutting points
marking the segments to be combined into offsprings
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uniform crossover – each locus is consider as potential cutting point
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...
Mutation Operators
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real valued mutation – the main parameters to adjust are the mutation step size
and the mutation probability – usually chosen to be 1/n, n – no. of variables
variable step mutation – small step mutations are accepted with high probability
while large step mutations are accepted with low probability
M[ loci ] = M[ loci ] + si * ri * pi , 1,n

si{ -1, +1 }

ri = r * |Bi – Ai|, r ~ 0.1, M[ loci ]  [Ai, Bi]
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pi =2-u*k, u[ 0, 1 ]
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step-size adaptation mutation – n step-sizes / one direction / n-directions; do
not offer consistent improvements for extreme multi-modal / high noise
functions
...
Guided Mutation - Mutation + LS
GA population
...
∂1 ∂2
...
...
∂i
...
∂n
∂i '
...
∂n
Angle Mutation
∂1 ∂2
...
Local Search
∂'1 ∂'2
...
Optimized Individual
∂'n
Termination Criteria
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preset number of fitness function evaluations /
number of generations
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termination once the evolution ceases
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optimal value has been found (!!)
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...
Distance - Levenberg-Marquardt
Distance - Diversification
Root Mean Squared Deviation
CONVERGENCE
GA population
...
DIVERSIFICATION
...
...
∂i
∂j
...
∂k
Compute Distances
between Conformations
Levenberg-Marquadt
RMSD(i,j) RMSD(i,k) RMSD(j,k)
Transform Conformations
...
∂i
∂j
...
∂k
 Ananth Ranganathan, The Levenberg-Marquardt Algorithm, citeseer.ist.psu.edu/638988.html, June 2004
Literature examples (A)
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Dusan P. Djurdjevic, Mark J. Biggs, Ab Initio Protein Fold Prediction Using Evolutionary
Algorithms: Influence of Design and Control Parameters on Performance, DOI
10.1002/jcc.20440, Wiley Interscience

Generational Evolutionary Algorithm
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Steady-State Evolutionary Algorithm
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tournament selection, single-point/multi-point and uniform crossover, uniform distribution
mutation with angles within the range of 0-360, termination in case of lack of improvement
for a specified number of generations
Christopher D. Rosin University of California, San Diego, A Comparison of Global and Local
Search Methods in Drug Docking, Proceedings of the Seventh International Conference on
Genetic Algorithms, ICGA, 1997

Simulated Annealing – 100 tests with 1.5~1.8 billion function evaluations per test, linearly
reduced temperature
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Solis and Wet’s Algorithm (a class of local and global search algorithms) – part of the GA+LS
hybrid, deviations chosen from a normal distribution - does not require gradient information

Genetic Algorithm+LS – local search applied to only 7% of the population in each generation;
stochastic selection, two-points crossover, Cauchy-deviate mutation
Literature examples (B)

Garrett M. Morris et al, Automated Docking Using a Lamarckian Genetic Algorithm and an
Empirical Binding Free Energy Function, Journal of Computational Chemistry, Vol. 19, No. 14,
1639-1662, 1998.

Monte Carlo – applied on a pre-calculated grid of interaction energies
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Simulated Annealing
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Genetic Algorithm, Lamarckian Genetic Algorithm
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uniform random value initialization (-180.0, 180.0),
maximum number of generations/function evaluations,
proportional elitist selection, constant-size population,
two-point crossover with breaks between genes, Cauchy distribution-based mutation,
local search at the end of each generation on a user-defined percentage of the population
each method is given ~1.5 million function evaluations / up to 41.5 minutes on a 200MHz
Silicon Graphics MIPS with 128 Mb of RAM
Outline


Protein Structure Prediction (PSP): modeling
and complexity analysis
Parallel Hybrid Metaheuristics for the PSP
Problem

Grid experimentation on GRID5000

Conclusion and Future Work
PARAllel and DIStributed Evolving Objects
http://paradiseo.gforge.inria.fr/
ParadisEO for computational Grids
Evolving Objects
framework (EO)
MO
MOEO
PVM, PThreads
MPI (LAM, CH)
Condor-MW Globus
Transparent use
EO
European project
(Geneura Team, http://eodev.sourceforge.net
INRIA, LIACS)
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Our contributions
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Multi-Objective EO (MOEO) for the design
of multi-objective evolutionary algorithms
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Moving Objects (MO) for the design of local
search algorithms
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ParadisEO for parallel hybrid metaheuristics
Message passing (MPI, PVM)
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Clusters, Networks of Workstations,
Multi-programming (PThreads)
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Shared Memory Multi-processors (SMP)
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Clusters of SMPs (CLUMPS)
Parallel distributed computing
Grid computing
 Condor-MW and Globus
(MPICH-G2)
 S. Cahon, N. Melab and E-G. Talbi. ParadisEO: A Framework for the Reusable Design of Parallel and
Distributed Metaheuristics. Journal of Heuristics, Elsevier Science, Vol.10(3), pages 357-380, May 2004.
Contributions
ParadisEO-EO (A framework of Evolutionary Algorithms)
ParadisEO-MO
Solution-based
metaheuristics
ParadisEO -MOEO
Techniques
related to
ParadisEO-PEO
multi-objective
(parallel and distributed metaheuristics)
optimization
http://paradiseo.gforge.inria.fr
Hill Climbing
Parallelism
(Partitioning solutions at several steps)
Simulated Annealing
Tabu search
Cooperation/Hybridization
(Algorithms exchange solutions)
Ex. Island cooperation
Development of hybrid metaheuristics
Hybrid metaheuristic
Independently cooperating
metaheuristics
High level
Trivial
Encapsulated metaheuristics
Unifying view of the three parallel
hierarchical levels

Low level
Inheritance relationships
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ParadisEO
distributed multi-agent model
Relay
Coevolutionary
Metaheuristics executed
in sequence
Concurrently executing
metaheuristics
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The deployment of concurrent
independent/cooperative
metaheuristics
The parallelization of a single step
of the metaheuristic (based on
distribution of the handled
solutions)
The parallelization of the processing
of a single solution
E-G. Talbi, « A taxonomy of hybrid metaheuristics », Journal of Heuristics, 2002.
Design of several levels of
parallelization/hybridization
Processing of a single solution
(Objective / Data partitioning)
Independent walks,
Multi-start model,
Hybridization/Cooperation
of metaheuristics
Parallel evaluation of
the neighborhood/population
Scalability
Heuristic
H PS
Population / Neighborhood
Solution
Parallel asynchronous hierarchical
Lamarkian GA
Cooperative GAs (Island
model)
Parallel
evaluation of
the population
Low-level coevolutionary
hybridization
Asynchronous Island Model
E.A.
E.A.
Migration
E.A.
E.A.
The Parallel Evaluation of the Population
E.A.
Solution
Full
fitness
Parallel Hybrid Simulated Annealing
Synchronous Multi-Start Model
Parallel Neighbourhood Exploration
Generate S0
k := 0
while Tk > Tthreshold do
for s:=1 to nbSamples do
Srand := randomMove( S0 );
ΔE := eval( Srand ) - eval( S0 );
if ΔE < 0 then
S0 := Srand;
else
S0 := Srand with prob. 1.0 / ( 1.0 + eΔE/Tk);
endif
endfor
k := k + 1; Adjust Tk;
endwhile
Gradient Local Search Optimization
Outline


Protein Structure Prediction (PSP):
modeling and complexity analysis
Parallel Hybrid Metaheuristics for the PSP
Problem

Grid experimentation on GRID5000

Conclusion and Future Work
Deployment of ParadisEO-G4
• Lille, Nice-Sophia Antipolis, Lyon, Nancy,
Rennes:
400CPUs
CLUSTER
A
M2
1. Reservation of the desired nodes
• exclusive reservation – no interference may occur; the
M1
processor are completely available during the reservation
time.
FRONTALE
2. Select a master node for the Globus GRID
M6
M3
3. Configure the Globus GRID (certificates,
user credentials, xinetd, postgresql, etc.)
M4
4. Deployment and execution – MPICH-G2
M5
CLUSTER C
CLUSTER B
M2
M2
M3
M1
FRONTALE
M6
M3
M1
FRONTALE
M4
M6
M4
GRID5000: A fully reconfigurable grid!M5 The configuration
phase relies on the deployment of pre-built Linux « images »
having Globus and MPICH-G2 already installed.
M5
1L2Y and α-Cyclodextrin
Triptophan-Cage –
Protein Data Bank ID: 1L2Y
α-β-γ Cyclodextrin
Parallel asynchronous hierarchical
Lamarkian GA - Cyclodextrin
 Native energy at 242 kcal mol-1
MAX_GEN
POPULATION
100
300
CXOVER
CMUTATION
LS_XOVER
LS_MUTATION
0.95
0.05
0.15
0.05
XOVER
MUTATION
1.0
0.1
MIGRATION_R
MIGRATION_STEP
15%
5Gen
Parallel Hybrid SA – 1L2Y &
Cyclodextrin
 the same number of individuals is sampled by both algorithms, GA and
SA
TRYPTOPHAN-CAGE (1L2Y) GA vs. SA
CYCLODEXTRIN SA
Cyclodextrin Native Energy: 242.4 kcal mol-1
Tryptophan-cage Native Energy: 46.446 kcal mol-1
Experimental Results
Cyclodextrin
Min
Max
Avg
StDev
GA
2470
5845.27
3790.56
708.54
SA
1029.14
4593
2359.26
281.2
Random+LS
1204
6329.91
3132.8
800.85
GA+LS, no isl.
GA+LS
SA
SA+LS
Cyclodextrin
264.599
164.973
1029.14
904.046
1L2Y
93.5521
57.5447
201.37
86.3106
Min
Max
Avg
StDev
1012.54
13471.4
4420.91
1521.67
Random Search 1L2Y
GA+LS, Island
GA+LS,No Ils.
GA+Isl.
GA
Cyclodextrin
31m20s
29m32s
7m58s
7m24s
1L2Y
29m57s
31m17s
7m47s
7m45s
Outline


Protein Structure Prediction (PSP):
modeling and complexity analysis
Parallel Hybrid Metaheuristics for the PSP
Problem

Grid experimentation on GRID5000

Conclusion and Future Work
Conclusion and Future Work (1)

The GA behaves better on the considered
benchmarks, especially on Cyclodextrin
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Bellow native-conformation energy results were
obtained
The SA results are comparable to the results
obtained by the GA with no hybridization
The SA allows for little intrinsic parallelism …
… but, it is not getting easily trapped in local
minima (like directed LS, i.e. gradient LS)
Conclusion and Future Work (2)


Hierarchical multi-stage parallel models
may prove to be efficient on the considered
benchmarks
Hybridization schemes combining …


… the GA (strong exploration capabilities)
… and the SA (intensification + less prone to
getting trapped in local minima than gradient
methods)
ParadisEO-PEO Implementation
ParadisEO-PEO Evolutionary Algorithm
evaluation function - the designed class has
to be derived from the peoPopEval class
peoEA (
eoContinue< EOT >&
__cont,
peoPopEval< EOT >&
__pop_eval,
eoSelect< EOT >&
__select,
peoTransform< EOT >& __transform,
eoReplacement< EOT >& __replace
);
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selection strategy – applied at each iteration
in order to obtain the offspring population
pop_eval ( population );
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transformation operators - crossover
operator(s), mutation operator(s); peoEA
requires a peoTransform derived object
do {


eoPop< EOT > offsprings;
select ( population, offsprings );
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
peoSeqPopEval
peoParaPopEval
peoSeqTransform
peoParaSGATransform
transform ( offsprings );
pop_eval ( offsprings );

replacement strategy – for integrating the
offsprings back into the initial population

continuation criterion - maximum number of
generations, checkpoints, etc.
replace ( population, offsprings );
} while ( cont ( population ) );
ParadisEO-PEO EA
Representation of the Individuals
#include <EO.h>
...
class Conformation : public EO< eoScalarFitness< double, greater< double > > > {
...
int operator[]( const int& index ) const {
}
...
};
return chromosome->getChromo( index+1 );
Individu* chromosome;
...
static Molecule* molecule;
static Hamiltonian* hamiltonian;
static Population* population;
extern void pack( const dockingAtGrid::Conformation& conformation );
extern void unpack( dockingAtGrid::Conformation& conformation );
Unpack Recv. Data
Pack Data to Send
ParadisEO-PEO EA
Representation – Packing & Unpacking
void pack( const dockingAtGrid::Conformation& conformation ) {
if ( conformation.invalid() )
pack( (unsigned int)0 );
else {
pack( (unsigned int)1 );
pack( conformation.fitness() );
}
}
for ( unsigned int index = 0; index < conformation.size(); index++ )
pack( conformation[ index ] );
void unpack( dockingAtGrid::Conformation& conformation ) {
eoScalarFitness<double, std::greater<double> > fitness;
unsigned int validFitness;
}
unpack( validFitness );
if ( validFitness ) {
unpack( fitness );
conformation.fitness( fitness );
} else {
...
}
M
I
D
L
E
W
A
R
E
ParadisEO-PEO EA
Random chromozome initialization
Random initialization for each of the active torsional angles with a value within a
specified angle domain (typically 0..360).
#include <eoInit.h>
...
class ConformationInit : public eoInit< Conformation > {
public:
...
void operator()( Conformation& conf ) {
for ( unsigned int i =1; i <= conf.molecule->getNvars(); i++ ) {
}
};
...
}
conf.chromosome->setChromo( i, eo::rng.random( ANGLE_DOMAIN ) );
Asynchronous Island Model
Parameters
// migrations will occur periodically at every N generations
eoPeriodicContinue < Conformation > mig_cont ( MIGRATIONS_AT_N_GENERATIONS );
// selection of the emigrant conformations is performed as a stochastic tournament
eoStochTournamentSelect < Conformation > mig_select_one;
eoSelectNumber < Conformation > mig_select ( mig_select_one, NUMBER_OF_MIGRANTS );
// migrations are merged with the population - the worse individuals are discarded
eoDeterministicSaDReplacement < Conformation > mig_elit_replace ( 0.3, 0.0, 0.1, 0.0 );
Async. Ils.
peoAsyncIslandMig < Conformation > island_mig (
mig_cont, mig_select, mig_elit_replace, ring_topology, alg.population, alg.population
);
EA
// the migration stream will follow a ring topology
RingTopology ring_topology;
// to be activated at the end of every generation
checkpoint->add ( island_mig );
peoEA < Conformation > eAlg (checkpoint, pop_eval, select, paraTransform, replacement );
island_mig.setOwner ( eAlg );
The Parallel Evaluation of the
Population
// functor for performing the evaluation of a specified conformation
ConformationEval conformationEval;
// wrapper for the evaluation functor – in addition it offers parallel evaluation
peoParaPopEval< Conformation > pop_eval ( conformationEval );
peoParaSGATransform< Conformation > paraTransform ( crossover, XOVER_RATE, mutation, MUTATION_RATE );
XML Grid Mapping
…
peoEA < Conformation > eAlg ( checkpoint, pop_eval, select, paraTransform, replacement );
<?xml version="1.0"?>
<schema>
<group scheduler="0">
<node name="0" num_workers="0">
</node>
<node name="1" num_workers="0">
<runner>1</runner>
...
</node>
<node name="2" num_workers="1">
</node>
<node name="3" num_workers="1">
...
</group>
</schema>
SCHEDULER
ALGORITHMS
WORKER NODES
End of story...