Genoud is a function that combines evolutionary search
algorithms with derivative-based (Newton or quasi-Newton) methods to
solve difficult optimization problems. Genoud may also be
used for optimization problems for which derivatives do not exist.
Genoud, via the cluster option, supports the use of
multiple computers, CPUs or cores to perform parallel computations.
genoud(fn, nvars, max=FALSE, pop.size=1000, max.generations=100,
wait.generations=10, hard.generation.limit=TRUE, starting.values=NULL,
MemoryMatrix=TRUE, Domains=NULL, default.domains=10,
solution.tolerance=0.001, gr=NULL, boundary.enforcement=0, lexical=FALSE,
gradient.check=TRUE, BFGS=TRUE, data.type.int=FALSE, hessian=FALSE,
unif.seed=round(runif(1, 1, 2147483647L)),
int.seed=round(runif(1, 1, 2147483647L)),print.level=2, share.type=0,
instance.number=0, output.path="stdout", output.append=FALSE,
project.path=NULL, P1=50, P2=50, P3=50, P4=50, P5=50, P6=50, P7=50,
P8=50, P9=0, P9mix=NULL, BFGSburnin=0, BFGSfn=NULL, BFGShelp=NULL,
control=list(), optim.method=ifelse(boundary.enforcement < 2, "BFGS",
"L-BFGS-B"), transform=FALSE, debug=FALSE, cluster=FALSE, balance=FALSE,
...)The function to be minimized (or maximized if
max=TRUE). The first argument of the function must be the
vector of parameters over which minimizing is to
occur. The function must return a scalar result (unless
lexical=TRUE).
For example, if we wish to maximize the sin()
function. We can simply call genoud by genoud(sin,
nvars=1,max=TRUE).
The number of parameters to be selected for the function to be minimized (or maximized).
Maximization (TRUE) or Minimizing (FALSE). Determines
if genoud minimizes or maximizes the objective function.
Population Size. This is the number of individuals genoud uses to
solve the optimization problem. There are several restrictions on
what the value of this number can be. No matter what population
size the user requests, the number is automatically adjusted to make
certain that the relevant restrictions are satisfied. These
restrictions originate
in what is required by several of the operators. In particular,
operators 6 (Simple Crossover) and 8 (Heuristic
Crossover) require an even number of individuals to work on---i.e., they
require two parents. Therefore, the pop.size variable and the
operators sets must be such that these three operators have an even
number of individuals to work with. If this does not occur, the
population size is automatically increased until this constraint is
satisfied.
Maximum Generations. This is the maximum number of generations that
genoud will run when attempting to optimize a function. This is a
soft limit. The maximum generation limit will be binding for
genoud only if hard.generation.limit has
been set equal to TRUE. If it has not been set equal to
TRUE, two soft triggers control when genoud stops:
wait.generations and gradient.check.
Although the max.generations variable is not, by default,
binding, it is nevertheless important because many operators use it
to adjust
their behavior. In essence, many of the operators become less random
as the generation count gets closer to the max.generations
limit. If
the limit is hit and genoud decides to
continue working, genoud automatically increases the
max.generation
limit.
Please see MemoryMatrix for some important interactions
with memory management.
If there is no improvement in the objective function in this number
of generations, genoud will think that it has
found the optimum. If the
gradient.check trigger has been
turned on, genoud will only start counting wait.generations
if the gradients are within
solution.tolerance of zero. The
other variables controlling termination are
max.generations and hard.generation.limit.
This logical variable determines if the max.generations
variable is a binding constraint for genoud. If
hard.generation.limit is FALSE, then genoud may exceed
the max.generations count if either the objective function
has improved within a given number of generations (determined by
wait.generations) or if the gradients are not zero
(determined by gradient.check).
Please see MemoryMatrix for some important interactions
with memory management.
A vector or matrix containing parameter values
which genoud will use at startup. Using this option, the user
may insert one or more individuals into the starting population. If a
matrix is provided, the columns should be the variables and the rows
the individuals. genoud will randomly create the other
individuals.
This variable controls if genoud sets up a memory matrix. Such a
matrix ensures that genoud will request the fitness evaluation of a
given set of parameters only once. The variable may
be TRUE or FALSE. If it is FALSE, genoud will
be aggressive in conserving memory. The most significant negative
implication of this variable being set to FALSE is that
genoud will no longer maintain a memory matrix of all evaluated
individuals. Therefore, genoud may request evaluations which it has
already previously requested.
Note that when nvars or pop.size are large, the memory
matrix consumes a large amount of RAM. Genoud's memory matrix will
require somewhat less memory if the user sets
hard.generation.limit equal to TRUE.
This is a nvars \(\times 2\)
matrix. For each variable, in the first column is the lower bound and
in the second column the upper bound. None of genoud's
starting population will be
generated outside of the bounds. But some of the operators may
generate children which
will be outside of the bounds unless the
boundary.enforcement flag is
turned on.
If the user does not provide any values for Domains, genoud will setup
default domains using default.domains.
For linear and nonlinear constraints please see the discussion in
the Note section.
If the user does not want to provide a Domains matrix,
domains may nevertheless be set by the user with this easy to use
scalar option. Genoud will create a
Domains matrix by setting the lower bound for all of the parameters
equal to -1 \(\times\) default.domains and the upper
bound equal to default.domains.
This is the tolerance level used by genoud. Numbers within
solution.tolerance are considered to be equal. This is
particularly
important when it comes to evaluating wait.generations and
conducting the gradient.check.
A function to provide the gradient for the BFGS
optimizer. If it is NULL, numerical gradients will be used
instead.
This variable determines the degree to which genoud obeys the
boundary constraints. Notwithstanding the value of the variable,
none of genoud's starting population values will be outside
of the bounds.
boundary.enforcement has three possible values: 0 (anything goes), 1
(partial), and 2 (no trespassing):
0: Anything Goes This option allows any of the operators to create out-of-bounds individuals and these individuals will be included in the population if their fit values are good enough. The boundaries are only important when generating random individuals.
1: partial enforcement This allows operators (particularly those operators which use the derivative based optimizer, BFGS) to go out-of-bounds during the creation of an individual (i.e., out-of-bounds values will often be evaluated). But when the operator has decided on an individual, it must be in bounds to be acceptable.
2: No Trespassing
No out-of-bounds evaluations will ever be requested. In this
case, boundary enforcement is also applied to the BFGS
algorithm, which prevents candidates from straying beyond the
bounds defined by Domains. Note that this forces the use
of the L-BFGS-B algorithm for optim.
This algorithm requires that all fit values and gradients be
defined and finite for all function evaluations. If this causes
an error, it is suggested that the BFGS algorithm be used
instead by setting boundary.enforcement=1.
This option enables lexical optimization. This is
where there are multiple fit criteria and the parameters are chosen so
as to maximize fitness values in lexical order---i.e., the second fit
criterion is only relevant if the parameters have the same fit for the
first etc. The fit function used with this option should return a
numeric vector of fitness values in lexical order. This option
can take on the values of FALSE, TRUE or an integer
equal to the number of fit criteria which are returned by fn.
The value object which is returned by genoud will
include all of the fit criteria at the solution. The
GenMatch function makes extensive use of this
option.
If this variable is TRUE, genoud will not start counting
wait.generations unless each gradient is
solution.tolerance close to zero. This
variable has no effect if the max.generations limit has been
hit and the hard.generation.limit option has been set to
TRUE. If BFGSburnin < 0, then it will be ignored unless
gradient.check = TRUE.
This variable denotes whether or not genoud applies a
quasi-Newton derivative optimizer (BFGS) to the best individual at
the end of each generation after the initial one. See the
optim.method option to change the optimizer. Setting BFGS to
FALSE does not mean that the BFGS will never be used. In
particular, if you want BFGS never to be used, P9 (the
Local-Minimum Crossover operator) must also be set to zero.
This option sets the data type of the parameters of the function to
be optimized. If the variable is TRUE, genoud will
search over integers when it optimizes the parameters.
With integer parameters, genoud never uses derivative
information. This implies that the BFGS quasi-Newton optimizer is
never used---i.e., the BFGS flag is set to FALSE. It
also implies
that Operator 9 (Local-Minimum Crossover) is set to zero and that
gradient checking (as a convergence criterion) is turned off. No
matter what other options have been set to,
data.type.int takes precedence---i.e., if genoud is told that
it is searching over an integer parameter space, gradient
information is never considered.
There is no option to mix integer and floating point parameters. If
one wants to mix the two, it is suggested that the user pick integer type
and in the objective function map a particular integer range into a
floating point number range. For example, tell genoud to search
from 0 to 100 and divide by 100 to obtain a search grid of 0 to 1.0
(by .1).
Alternatively, the user could use floating point numbers and round
the appropriate parameters to the nearest integer inside fn
before the criterion (or criteria if lexical = TRUE) is
evaluated. In that case, the transform option can be used to
create the next generation from the current generation when the
appropriate parameters are in the rounded state.
When this flag is set to TRUE, genoud will return the
hessian matrix at the solution as part of its return list. A user
can use this matrix to calculate standard errors.
This should not be set. To set the seed, one should use
the set.seed() function directly. It only exists to ensure compatibility
with functions which are used to call the older version of this function.
See unif.seed.
This variable controls the level of printing that genoud does. There
are four possible levels: 0 (minimal printing), 1 (normal), 2
(detailed), and 3 (debug). If level 2 is selected, genoud will
print details about the population at each generation. The
print.level variable also significantly affects how much
detail is placed in the project file---see project.path.
Note that R convention would have
us at print level 0 (minimal printing). However, because genoud
runs may take a long time, it is important for the user to receive
feedback. Hence, print level 2 has been set as the default.
If share.type is equal to 1, then genoud, at
startup, checks to see if there is an existing project file (see
project.path). If such a file exists, it initializes its
original population using it. This option can be used neither with
the lexical nor the transform options.
If the project file contains a smaller population than the current
genoud run, genoud will randomly create the necessary individuals. If
the project file contains a larger population than the current genoud
run, genoud will kill the necessary individuals using exponential
selection.
If the number of variables (see nvars)
reported in the project file is different from the current genoud run,
genoud does not use the project file (regardless of the value of
share.type) and genoud generates the necessary starting
population at random.
This number (starting from 0) denotes the number of recursive
instances of genoud. genoud then sets up its random number
generators and other such structures so that the multiple instances
do not interfere with each other. It is
up to the user to make certain that the different instances of
genoud are not writing to
the same output file(s): see project.path.
For the R version of genoud this variable is of limited
use. It is basically there in case a genoud run is being
used to optimize the result of another genoud run (i.e., a
recursive implementation).
This option is no longer supported. It used to
allow one to redirect the output. Now please use
sink. The option remains in order to provide
backward compatibility for the API.
This option is no longer supported. Please see
sink. The option remains in order to provide
backward compatibility for the API.
This is the path of the genoud project
file. The project file prints one individual per line with the fit
value(s) printed first and then the parameter values. By default
genoud places its output in a file called "genoud.pro"
located in the temporary directory provided by
tempdir. The behavior of the project file depends
on the print.level chosen. If the print.level
variable is set to 1, then the project file is rewritten after
each generation. Therefore, only the currently fully completed
generation is included in the file. If the print.level
variable is set to 2, then each new generation is simply appended
to the project file. For print.level=0, the project file
is not created.
This is the cloning operator.
genoud always clones the best individual each generation.
But this operator clones others as well. Please see the Operators
Section for details about operators and how they are weighted.
This is the uniform mutation operator. One parameter of the parent is mutated. Please see the Operators Section for details about operators and how they are weighted.
This is the boundary mutation operator. This operator finds a parent and mutates one of its parameters towards the boundary. Please see the Operators Section for details about operators and how they are weighted.
Non-Uniform Mutation. Please see the Operators Section for details about operators and how they are weighted.
This is the polytope crossover. Please see the Operators Section for details about operators and how they are weighted.
Simple Crossover. Please see the Operators Section for details about operators and how they are weighted.
Whole Non-Uniform Mutation. Please see the Operators Section for details about operators and how they are weighted.
Heuristic Crossover. Please see the Operators Section for details about operators and how they are weighted.
Local-Minimum Crossover: BFGS. This is rather CPU intensive, and should be generally used less than the other operators. Please see the Operators Section for details about operators and how they are weighted.
This is
a tuning parameter for the P9 operator. The local-minimum
crossover operator by default takes the convex combination of the
result of a BFGS optimization and the parent individual. By
default the mixing (weight) parameter for the convex combination
is chosen by a uniform random draw between 0 and 1. The
P9mix option allows the user to select this mixing
parameter. It may be any number greater than 0 and less than or
equal to 1. If 1, then the BFGS result is simply used.
The number of generations which are run before
the BFGS is first used. Premature use of the BFGS can lead to
convergence to a local optimum instead of the global one. This
option allows the user to control how many generations are run
before the BFGS is started and would logically be a non-negative
integer. However, if BFGSburnin < 0, the BFGS will be used
if and when wait.generations is doubled because at least
one gradient is too large, which can only occur when
gradient.check = TRUE. This option delays the use of both
the BFGS on the best individual and the P9 operator.
This is a function for the BFGS optimizer to
optimize, if one wants to make it distinct from the fn
function. This is useful when doing lexical optimization
because otherwise a derivative based optimizer cannot be used
(since it requires a single fit value). It is suggested that if
this functionality is being used, both the fn and
BFGSfn functions obtain all of the arguments they need
(except for the parameters being optimized) by lexical scope
instead of being passed in as arguments to the functions.
Alternatively, one may use the BFGShelp option to pass
arguments to BFGSfn. If print.level > 2, the results
from the BFGS optimizer are printed every time it is called.
An optional function to pass arguments to
BFGSfn. This function should take an argument named
`initial', an argument named `done' that defaults to FALSE,
or at least allow ... to be an argument. BFGSfn
must have an argument named `helper' if BFGShelp is used
because the call to optim includes the hard-coded
expression helper = do.call(BFGShelp, args = list(initial =
foo.vals), envir = environment(fn))), which evaluates the
BFGShelp function in the environment of BFGSfn
(fn is just a wrapper for BFGSfn) at par =
foo.vals where foo.vals contains the starting values for
the BFGS algorithm. The `done' argument to BFGSfn is used
if the user requests that the Hessian be calculated at the
genoud solution.
A character string among those that are admissible for the
method argument to the optim function, namely one of
"BFGS", "L-BFGS-B", "Nelder-Mead", "CG", or "SANN".
By default, optim.method is "BFGS" if boundary.enforcement < 2
and is "L-BFGS-B" if boundary.enforcement = 2. For discontinuous
objective functions, it may be advisable to select "Nelder-Mead" or "SANN".
If selecting "L-BFGS-B" causes an error message, it may be advisable to
select another method or to adjust the control argument. Note that the various
arguments of genoud that involve the four letters “BFGS” continue to
be passed to optim even if optim.method != "BFGS".
A logical that defaults to FALSE. If
TRUE, it signifies that fn will return a numeric
vector that contains the fit criterion (or fit criteria if
lexical = TRUE), followed by the parameters. If this option
is used, fn should have the following general form in
its body:
par <- myTransformation(par)
criter <- myObjective(par)
return( c(criter, par) )
This option is useful when parameter transformations are necessary
because the next generation of the population will be created from
the current generation in the transformed state, rather than the
original state. This option can be used by users to implement their
own operators.
There are some issues that should be kept in mind. This option cannot
be used when data.type.int = TRUE. Also, this option coerces
MemoryMatrix to be FALSE, implying that the cluster
option cannot be used. And, unless BFGSfn is specified, this option coerces
gradient.check to FALSE, BFGS to FALSE,
and P9 to 0. If BFGSfn is specified, that function should
perform the transformation but should only return a scalar fit criterion,
for example:
par <- myTransformation(par)
criter <- myCriterion(par)
return(criter)
Finally, if boundary.enforcement > 0, care must be taken to
assure that the transformed parameters are within the Domains,
otherwise unpredictable results could occur. In this case, the transformations are
checked for consistency with Domains but only in the initial generation
(to avoid an unacceptable loss in computational speed).
This
variable turns on some debugging information. This variable may
be TRUE or FALSE.
This can either be an
object of the 'cluster' class returned by one of the
makeCluster commands in the parallel package or a
vector of machine names so genoud can setup the cluster
automatically. If it is the latter, the vector should look like:
c("localhost","musil","musil","deckard"). This
vector would create a cluster with four nodes: one on the
localhost another on "deckard" and two on the machine named
"musil". Two nodes on a given machine make sense if the machine
has two or more chips/cores. genoud will setup a SOCK
cluster by a call to makePSOCKcluster. This
will require the user to type in her password for each node as the
cluster is by default created via ssh. One can add on
usernames to the machine name if it differs from the current
shell: "username@musil". Other cluster types, such as PVM and
MPI, which do not require passwords can be created by directly
calling makeCluster, and then passing the
returned cluster object to genoud. For an example of how to
manually setup up a cluster with a direct call to
makeCluster see
http://sekhon.berkeley.edu/rgenoud/R/genoud_cluster_manual.R.
For an example of how to get around a firewall by ssh tunneling
see:
http://sekhon.berkeley.edu/rgenoud/R/genoud_cluster_manual_tunnel.R.
This logical flag controls if load balancing is done across the cluster. Load balancing can result in better cluster utilization; however, increased communication can reduce performance. This option is best used if the function being optimized takes at least several minutes to calculate or if the nodes in the cluster vary significantly in their performance. If cluster==FALSE, this option has no effect.
Further arguments to be passed to fn and
gr.
genoud returns a list
with 7 objects. 8 objects are returned if the user has requested
the hessian to be calculated at the solution. Please see the
hessian option. The returned objects are:
This variable contains the fitness value at the solution. If
lexical optimization was requested, it is a vector.
This vector contains the parameter values found at the solution.
This vector contains the gradients found at the solution. If no
gradients were calculated, they are reported to be NA.
This variable contains the number of generations genoud ran for.
This variable contains the generation number at which genoud found
the solution.
This variable contains the population size that genoud actually used.
See pop.size for why this value may differ from the
population size the user requested.
This vector reports the actual number of operators (of each type)
genoud used. Please see the Operators Section for details.
If the user has requested the hessian
matrix to be returned (via the hessian flag), the hessian
at the solution will be returned. The user may use this matrix to calculate standard
errors.
Genoud has nine operators that it uses. The integer values which are
assigned to each of these operators (P1\(\cdots\)P9) are
weights.
Genoud calculates the sum of \(s = P1+P2+\cdots+P9\). Each operator is
assigned a weight equal to \(W_{n} = \frac{s}{P_{n}}\). The number of
times an operator is called usually equals \(c_{n} = W_{n} \times
pop.size\).
Operators 6 (Simple Crossover) and 8 (Heuristic
Crossover) require an even number of individuals to work on---i.e.,
they require two parents. Therefore, the pop.size variable and
the operators sets must be such that these three operators have an
even number of individuals to work with. If this does not occur,
genoud automatically upwardly adjusts the population size to make this
constraint hold.
Strong uniqueness checks have been built into the operators to help ensure that the operators produce offspring different from their parents, but this does not always happen.
Note that genoud always keeps the best individual each generation.
genoud's 9 operators are:
Cloning
Uniform Mutation
Boundary Mutation
Non-Uniform Crossover
Polytope Crossover
Simple Crossover
Whole Non-Uniform Mutation
Heuristic Crossover
Local-Minimum Crossover: BFGS
For more information please see Table 1 of the reference article: http://sekhon.berkeley.edu/papers/rgenoudJSS.pdf.
Genoud solves problems that are nonlinear or
perhaps even discontinuous in the parameters of the function to be
optimized. When a statistical model's estimating function (for
example, a log-likelihood) is nonlinear in the model's parameters,
the function to be optimized will generally not be globally
concave and may have irregularities such as saddlepoints or
discontinuities. Optimization methods that rely on derivatives of
the objective function may be unable to find any optimum at all.
Multiple local optima may exist, so that there is no guarantee
that a derivative-based method will converge to the global
optimum. On the other hand, algorithms that do not use derivative
information (such as pure genetic algorithms) are for many
problems needlessly poor at local hill climbing. Most statistical
problems are regular in a neighborhood of the solution.
Therefore, for some portion of the search space, derivative
information is useful for such problems. Genoud also works
well for problems that no derivative information exists. For
additional documentation and examples please see
http://sekhon.berkeley.edu/rgenoud.
Mebane, Walter R., Jr. and Jasjeet S. Sekhon. 2011. "Genetic Optimization Using Derivatives: The rgenoud Package for R." Journal of Statistical Software, 42(11): 1-26. http://www.jstatsoft.org/v42/i11/
Sekhon, Jasjeet Singh and Walter R. Mebane, Jr. 1998. ``Genetic Optimization Using Derivatives: Theory and Application to Nonlinear Models.'' Political Analysis, 7: 187-210. http://sekhon.berkeley.edu/genoud/genoud.pdf
Mebane, Walter R., Jr. and Jasjeet S. Sekhon. 2004. ``Robust Estimation and Outlier Detection for Overdispersed Multinomial Models of Count Data.'' American Journal of Political Science, 48 (April): 391-410. http://sekhon.berkeley.edu/multinom.pdf
Bright, H. and R. Enison. 1979. Quasi-Random Number Sequences from a Long-Period TLP Generator with Remarks on Application to Cryptography. Computing Surveys, 11(4): 357-370.
# NOT RUN {
#maximize the sin function
sin1 <- genoud(sin, nvars=1, max=TRUE)
#minimize the sin function
sin2 <- genoud(sin, nvars=1, max=FALSE)
# }
# NOT RUN {
#maximize a univariate normal mixture which looks like a claw
claw <- function(xx) {
x <- xx[1]
y <- (0.46*(dnorm(x,-1.0,2.0/3.0) + dnorm(x,1.0,2.0/3.0)) +
(1.0/300.0)*(dnorm(x,-0.5,.01) + dnorm(x,-1.0,.01) + dnorm(x,-1.5,.01)) +
(7.0/300.0)*(dnorm(x,0.5,.07) + dnorm(x,1.0,.07) + dnorm(x,1.5,.07)))
return(y)
}
claw1 <- genoud(claw, nvars=1,pop.size=3000,max=TRUE)
# }
# NOT RUN {
# }
# NOT RUN {
#Plot the previous run
xx <- seq(-3,3,.05)
plot(xx,lapply(xx,claw),type="l",xlab="Parameter",ylab="Fit",
main="GENOUD: Maximize the Claw Density")
points(claw1$par,claw1$value,col="red")
# Maximize a bivariate normal mixture which looks like a claw.
biclaw <- function(xx) {
mNd2 <- function(x1, x2, mu1, mu2, sigma1, sigma2, rho)
{
z1 <- (x1-mu1)/sigma1
z2 <- (x2-mu2)/sigma2
w <- (1.0/(2.0*pi*sigma1*sigma2*sqrt(1-rho*rho)))
w <- w*exp(-0.5*(z1*z1 - 2*rho*z1*z2 + z2*z2)/(1-rho*rho))
return(w)
}
x1 <- xx[1]+1
x2 <- xx[2]+1
y <- (0.5*mNd2(x1,x2,0.0,0.0,1.0,1.0,0.0) +
0.1*(mNd2(x1,x2,-1.0,-1.0,0.1,0.1,0.0) +
mNd2(x1,x2,-0.5,-0.5,0.1,0.1,0.0) +
mNd2(x1,x2,0.0,0.0,0.1,0.1,0.0) +
mNd2(x1,x2,0.5,0.5,0.1,0.1,0.0) +
mNd2(x1,x2,1.0,1.0,0.1,0.1,0.0)))
return(y)
}
biclaw1 <- genoud(biclaw, default.domains=20, nvars=2,pop.size=5000,max=TRUE)
# }
# NOT RUN {
# For more examples see: http://sekhon.berkeley.edu/rgenoud/R.
# }
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