Package 'evoper' reference manual

Title:	Evolutionary Parameter Estimation for 'Repast Simphony' Models
Description:	The EvoPER, Evolutionary Parameter Estimation for Individual-based Models is an extensible package providing optimization driven parameter estimation methods using metaheuristics and evolutionary computation techniques (Particle Swarm Optimization, Simulated Annealing, Ant Colony Optimization for continuous domains, Tabu Search, Evolutionary Strategies, ...) which could be more efficient and require, in some cases, fewer model evaluations than alternatives relying on experimental design. Currently there are built in support for models developed with 'Repast Simphony' Agent-Based framework (<https://repast.github.io/>) and with NetLogo (<https://ccl.northwestern.edu/netlogo/>) which are the most used frameworks for Agent-based modeling.
Authors:	Antonio Prestes Garcia [aut, cre], Alfonso Rodriguez-Paton [aut, ths]
Maintainer:	Antonio Prestes Garcia <[email protected]>
License:	MIT + file LICENSE
Version:	0.5.0
Built:	2024-10-30 06:53:35 UTC
Source:	CRAN

Ant colony optimization for continuous domains

Description

An implementation of Ant Colony Optimization algorithm for continuous variables.

Usage

abm.acor(objective, options = NULL)
abm.acor(objective, options = NULL)

Arguments

`objective`	An instance of ObjectiveFunction (or subclass) class ObjectiveFunction
`options`	An apropiate instance from a sublclass of Options class

References

[1] Socha, K., & Dorigo, M. (2008). Ant colony optimization for continuous domains. European Journal of Operational Research, 185(3), 1155-1173. http://doi.org/10.1016/j.ejor.2006.06.046

Examples

## Not run: 
 f<- PlainFunction$new(f0.rosenbrock2)

 f$Parameter(name="x1",min=-100,max=100)
 f$Parameter(name="x2",min=-100,max=100)

 extremize("acor", f)

## End(Not run)

## Not run: 
 f<- PlainFunction$new(f0.rosenbrock2)

 f$Parameter(name="x1",min=-100,max=100)
 f$Parameter(name="x2",min=-100,max=100)

 extremize("acor", f)

## End(Not run)

EvoPER Evolutionary Strategy 1

Description

This function tries to provide a rough approximation to best solution when no information is available for the correct range of input parameters for the objective function. It can useful for studying the behavior of individual-based models with high variability in the output variables showing nonlinear behaviors.

Usage

abm.ees1(objective, options = NULL)
abm.ees1(objective, options = NULL)

Arguments

`objective`	An instance of ObjectiveFunction (or subclass) class ObjectiveFunction
`options`	An apropiate instance from a sublclass of Options class

Examples

## Not run: 
 f<- PlainFunction$new(f0.rosenbrock2)

 f$Parameter(name="x1",min=-100,max=100)
 f$Parameter(name="x2",min=-100,max=100)

 extremize("ees1", f)

## End(Not run)

## Not run: 
 f<- PlainFunction$new(f0.rosenbrock2)

 f$Parameter(name="x1",min=-100,max=100)
 f$Parameter(name="x2",min=-100,max=100)

 extremize("ees1", f)

## End(Not run)

EvoPER Evolutionary Strategy 2

Description

Usage

abm.ees2(objective, options = NULL)
abm.ees2(objective, options = NULL)

Arguments

`objective`	An instance of ObjectiveFunction (or subclass) class ObjectiveFunction
`options`	An apropiate instance from a sublclass of Options class

Examples

## Not run: 
 f<- PlainFunction$new(f0.rosenbrock2)

 f$Parameter(name="x1",min=-100,max=100)
 f$Parameter(name="x2",min=-100,max=100)

 extremize("ees2", f)

## End(Not run)

## Not run: 
 f<- PlainFunction$new(f0.rosenbrock2)

 f$Parameter(name="x1",min=-100,max=100)
 f$Parameter(name="x2",min=-100,max=100)

 extremize("ees2", f)

## End(Not run)

abm.pso

Description

An implementaion of Particle Swarm Optimization method for parameter estimation of Individual-based models.

Usage

abm.pso(objective, options = NULL)
abm.pso(objective, options = NULL)

Arguments

`objective`	An instance of ObjectiveFunction (or subclass) class ObjectiveFunction
`options`	An apropiate instance from a sublclass of Options class

References

[1] Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of ICNN 95 - International Conference on Neural Networks (Vol. 4, pp. 1942-1948). IEEE.

[2] Poli, R., Kennedy, J., & Blackwell, T. (2007). Particle swarm optimization. Swarm Intelligence, 1(1), 33-57.

Examples

## Not run: 
 f<- PlainFunction$new(f0.rosenbrock2)

 f$Parameter(name="x1",min=-100,max=100)
 f$Parameter(name="x2",min=-100,max=100)

 extremize("pso", f)

## End(Not run)

## Not run: 
 f<- PlainFunction$new(f0.rosenbrock2)

 f$Parameter(name="x1",min=-100,max=100)
 f$Parameter(name="x2",min=-100,max=100)

 extremize("pso", f)

## End(Not run)

abm.saa

Description

An implementation of Simulated Annealing Algorithm optimization method for parameter estimation of Individual-based models.

Usage

abm.saa(objective, options = NULL)
abm.saa(objective, options = NULL)

Arguments

`objective`	An instance of ObjectiveFunction (or subclass) class ObjectiveFunction
`options`	An apropiate instance from a sublclass of Options class

Value

The best solution.

References

[1] Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by Simulated Annealing. Science, 220(4598).

Examples

## Not run: 
 f<- PlainFunction$new(f0.rosenbrock2)

 f$Parameter(name="x1",min=-100,max=100)
 f$Parameter(name="x2",min=-100,max=100)

 extremize("saa", f)

## End(Not run)

## Not run: 
 ## A Repast defined function
 f<- RepastFunction$new("/usr/models/BactoSim(HaldaneEngine-1.0)","ds::Output",300)

 ## or a plain function

 f1<- function(x1,x2,x3,x4) {
   10 * (x1 - 1)^2 + 20 * (x2 - 2)^2 + 30 * (x3 - 3)^2 + 40 * (x4 - 4)^2
 }

 f<- PlainFunction$new(f1)

 f$addFactor(name="cyclePoint",min=0,max=90)
 f$addFactor(name="conjugationCost",min=0,max=100)
 f$addFactor(name="pilusExpressionCost",min=0,max=100)
 f$addFactor(name="gamma0",min=1,max=10)

 abm.saa(f, 100, 1,  100, 0.75)

## End(Not run)

## Not run: 
 f<- PlainFunction$new(f0.rosenbrock2)

 f$Parameter(name="x1",min=-100,max=100)
 f$Parameter(name="x2",min=-100,max=100)

 extremize("saa", f)

## End(Not run)

## Not run: 
 ## A Repast defined function
 f<- RepastFunction$new("/usr/models/BactoSim(HaldaneEngine-1.0)","ds::Output",300)

 ## or a plain function

 f1<- function(x1,x2,x3,x4) {
   10 * (x1 - 1)^2 + 20 * (x2 - 2)^2 + 30 * (x3 - 3)^2 + 40 * (x4 - 4)^2
 }

 f<- PlainFunction$new(f1)

 f$addFactor(name="cyclePoint",min=0,max=90)
 f$addFactor(name="conjugationCost",min=0,max=100)
 f$addFactor(name="pilusExpressionCost",min=0,max=100)
 f$addFactor(name="gamma0",min=1,max=10)

 abm.saa(f, 100, 1,  100, 0.75)

## End(Not run)

Tabu Search metaheuristic

Description

An implementation of Tabu Search algorithm for parameter estimation

Usage

abm.tabu(objective, options = NULL)
abm.tabu(objective, options = NULL)

Arguments

`objective`	An instance of ObjectiveFunction (or subclass) class ObjectiveFunction
`options`	An apropiate instance from a sublclass of Options class

References

[1] Fred Glover (1989). "Tabu Search - Part 1". ORSA Journal on Computing, 190-206. doi:10.1287/ijoc.1.3.190. [2] Fred Glover (1990). "Tabu Search - Part 2". ORSA Journal on Computing, 4-32. doi:10.1287/ijoc.2.1.4.

Examples

## Not run: 
 f<- PlainFunction$new(f0.rosenbrock2)

 f$Parameter(name="x1",min=-100,max=100)
 f$Parameter(name="x2",min=-100,max=100)

 or

 f$Parameter0(name="x1",levels=c(0:4))
 f$Parameter0(name="x2",levels=c(-2,-1,0,1,2))

 extremize("tabu", f)

## End(Not run)

## Not run: 
 f<- PlainFunction$new(f0.rosenbrock2)

 f$Parameter(name="x1",min=-100,max=100)
 f$Parameter(name="x2",min=-100,max=100)

 or

 f$Parameter0(name="x1",levels=c(0:4))
 f$Parameter0(name="x2",levels=c(-2,-1,0,1,2))

 extremize("tabu", f)

## End(Not run)

acor.archive

Description

This function is used for creating and maintaining the ACOr archive 'T'. The function keeps the track of 'k' solotion in the archive.

Usage

acor.archive(s, f, w, k, T = NULL)
acor.archive(s, f, w, k, T = NULL)

Arguments

`s`	The solution 'ants'
`f`	The evaluation of solution
`w`	The weight vector
`k`	The archive size
`T`	The current archive

Value

The solution archive

References

[1] Socha, K., & Dorigo, M. (2008). Ant colony optimization for continuous domains. European Journal of Operational Research, 185(3), 1155-1173. http://doi.org/10.1016/j.ejor.2006.06.046

acor.F

Description

Helper function for extracting the 'F' function evaluations from archive ACOr 'T'

Usage

acor.F(T)
acor.F(T)

Arguments

`T`	The solution archive

Value

The F matrix

Select the lth gaussian function

Description

Given a weight vector calculate the probabilities of selecting the lth gaussian function and return the index of lht gaussian selected with probability p

Usage

acor.lthgaussian(W)
acor.lthgaussian(W)

Arguments

`W`	The vector of weights

Value

The index of lht gaussian function

References

[1] Socha, K., & Dorigo, M. (2008). Ant colony optimization for continuous domains. European Journal of Operational Research, 185(3), 1155-1173. http://doi.org/10.1016/j.ejor.2006.06.046

acor.N

Description

Helper function for getting the size of solution

Usage

acor.N(T)
acor.N(T)

Arguments

`T`	The solution archive

Value

The size 'n' of a solution 's'

Gaussian kernel choosing probability

Description

Calculate the probability of choosing the lth Gaussian function

Usage

acor.probabilities(W, l = NULL)
acor.probabilities(W, l = NULL)

Arguments

`W`	The vector of weights
`l`	The lth element of algorithm solution archive T

Value

The vector of probabilities 'p'

References

[1] Socha, K., & Dorigo, M. (2008). Ant colony optimization for continuous domains. European Journal of Operational Research, 185(3), 1155-1173. http://doi.org/10.1016/j.ejor.2006.06.046

acor.S

Description

Helper function for extracting solution 'S' from archive 'T'

Usage

acor.S(T)
acor.S(T)

Arguments

`T`	The solution archive

Value

The solution matrix

Sigma calculation for ACOr

Description

Calculate the value of sigma

Usage

acor.sigma(Xi, k, T)
acor.sigma(Xi, k, T)

Arguments

`Xi`	The algorithm parameter
`k`	The solution archive size
`T`	The solution archive

Value

The sigma value

References

[1] Socha, K., & Dorigo, M. (2008). Ant colony optimization for continuous domains. European Journal of Operational Research, 185(3), 1155-1173. http://doi.org/10.1016/j.ejor.2006.06.046

acor.updateants

Description

Update the solution using the gaussian kernel

Usage

acor.updateants(S, N, W, t.mu, t.sigma)
acor.updateants(S, N, W, t.mu, t.sigma)

Arguments

`S`	The current solution ants
`N`	The numnber of required ants in solution
`W`	The weight vector
`t.mu`	The 'mean' from solution archive
`t.sigma`	The value of sigma from solution archive

Value

The new solution ants

References

[1] Socha, K., & Dorigo, M. (2008). Ant colony optimization for continuous domains. European Journal of Operational Research, 185(3), 1155-1173. http://doi.org/10.1016/j.ejor.2006.06.046

acor.W

Description

Helper function for extracting the 'W' function evaluations from archive ACOr 'T'

Usage

acor.W(T)
acor.W(T)

Arguments

`T`	The solution archive

Value

The weight vector

Weight calculation for ant colony optimization

Description

Calculates the weight element of ACOr algorithm for the solution archive.

Usage

acor.weigth(q, k, l)
acor.weigth(q, k, l)

Arguments

`q`	The Algorithm parameter. When small best-ranked solution is preferred
`k`	The Archive size
`l`	The lth element of algorithm solution archive T

Value

A scalar or a vector with calculated weigth.

References

[1] Socha, K., & Dorigo, M. (2008). Ant colony optimization for continuous domains. European Journal of Operational Research, 185(3), 1155-1173. http://doi.org/10.1016/j.ejor.2006.06.046

assert

Description

The assert function stop the execution if the logical expression given by the parameter expresion is false.

Usage

assert(expresion, string)
assert(expresion, string)

Arguments

`expresion`	Some logical expression
`string`	The text message to show if expression does not hold

bestFitness

Description

Given a set S of N solutions created with sortSolution, this function returns the fitness component fot the best solution.

Usage

bestFitness(S)
bestFitness(S)

Arguments

`S`	The solution set

Value

The best fitness value

bestSolution

Description

Given a set S of N solutions created with sortSolution, this function returns the best solution found.

Usage

bestSolution(S)
bestSolution(S)

Arguments

`S`	The solution set

Value

The best solution

cbuf

Description

Simple implementation of a circular buffer.

Usage

cbuf(b, v, e)
cbuf(b, v, e)

Arguments

`b`	The variable holding the current buffer content
`v`	The new valued to be added to b
`e`	The length of circular buffer

Value

The buffer b plus the element v minus the least recently added element

compare.algorithms1

Description

Compare the number of function evalutions and convergence for the following optimization algorithms, ("saa","pso","acor","ees1").

Usage

compare.algorithms1(F, seeds = c(27, 2718282, 36190727, 3141593, -91190721,
  -140743, 1321))
compare.algorithms1(F, seeds = c(27, 2718282, 36190727, 3141593, -91190721,
  -140743, 1321))

Arguments

`F`	The function to be tested
`seeds`	The random seeds which will be used for testing algorithms

Examples

## Not run: 
 rm(list=ls())
 d.cigar4<- compare.algorithms1(f0.cigar4)
 d.schaffer4<- compare.algorithms1(f0.schaffer4)
 d.griewank4<- compare.algorithms1(f0.griewank4)
 d.bohachevsky4<- compare.algorithms1(f0.bohachevsky4)
 d.rosenbrock4<- compare.algorithms1(f0.rosenbrock4)

## End(Not run)

## Not run: 
 rm(list=ls())
 d.cigar4<- compare.algorithms1(f0.cigar4)
 d.schaffer4<- compare.algorithms1(f0.schaffer4)
 d.griewank4<- compare.algorithms1(f0.griewank4)
 d.bohachevsky4<- compare.algorithms1(f0.bohachevsky4)
 d.rosenbrock4<- compare.algorithms1(f0.rosenbrock4)

## End(Not run)

contourplothelper

Description

Simple helper function for countour plots

Usage

contourplothelper(d, x, y, z, nbins = 32, binwidth = c(10, 10),
  points = c(300, 300), title = NULL)
contourplothelper(d, x, y, z, nbins = 32, binwidth = c(10, 10),
  points = c(300, 300), title = NULL)

Arguments

`d`	A data frame.
`x`	A string with the dataframe column name for x axis.
`y`	A string with the dataframe column name for y axis.
`z`	A string with the dataframe column name for z axis.
`nbins`	The number bins. The default is 32.
`binwidth`	The binwidths for 'kde2d'. Can be an scalar or a vector.
`points`	The number of grid points. Can be an scalar or a vector.
`title`	The optional plot title. May be omited.

ees1.challenge

Description

Repeat the evalution of best solution to tacke with variability.

Usage

ees1.challenge(solution, objective)
ees1.challenge(solution, objective)

Arguments

`solution`	The Problem solution
`objective`	The objective function

ees1.explore

Description

Explore the solution space on the neighborhood of solution 's' in order to find a new best.

Usage

ees1.explore(s, weight, p = 0.01)
ees1.explore(s, weight, p = 0.01)

Arguments

`s`	The Problem solution
`weight`	The exploration intensity
`p`	The mutation probability

ees1.mating

Description

This function 'mix' the elements present in the solution. The parameter 'mu' controls the intensity of mixing. Low values give preference to best solution components and high values make the values being select randomly.

Usage

ees1.mating(solution, mu)
ees1.mating(solution, mu)

Arguments

`solution`	The Problem solution
`mu`	The mixing intensity ratio, from 0 to 1. The mix intensity controls de the probability of chosing a worst solutions

ees1.mating1

Description

Usage

ees1.mating1(solution, mu)
ees1.mating1(solution, mu)

Arguments

`solution`	The Problem solution
`mu`	The mixing intensity ratio, from 0 to 1. The mix intensity controls de the probability of chosing a worst solutions

ees1.mutation

Description

Performs the mutation on generated solution

Usage

ees1.mutation(solution, mates, p = 0.01)
ees1.mutation(solution, mates, p = 0.01)

Arguments

`solution`	The Problem solution
`mates`	The mixed parents
`p`	The mutation probability

ees1.recombination

Description

Performs the recombination on solution

Usage

ees1.recombination(solution, mates)
ees1.recombination(solution, mates)

Arguments

`solution`	The Problem solution
`mates`	The mixed parents

ees.selection

Description

Select the elements with best fitness but accept uphill moves with probability 'kkappa'.

Usage

ees1.selection(s0, s1, kkappa)
ees1.selection(s0, s1, kkappa)

Arguments

`s0`	The current best solution set
`s1`	The new solution
`kkappa`	The selection pressure

elog.debug

Description

Wrapper for logging debug messages.

Usage

elog.debug(...)
elog.debug(...)

Arguments

...

Variable number of arguments including a format string.

elog.error

Description

Wrapper for logging error messages.

Usage

elog.error(...)
elog.error(...)

Arguments

...

Variable number of arguments including a format string.

elog.info

Description

Wrapper for logging info messages.

Usage

elog.info(...)
elog.info(...)

Arguments

...

Variable number of arguments including a format string.

elog.level

Description

Configure the current log level

Usage

elog.level(level = NULL)
elog.level(level = NULL)

Arguments

level

The log level (ERROR|WARN|INFO|DEBUG)

Value

The log level

enforceBounds

Description

Checks if parameters fall within upper an lower bounds

Usage

enforceBounds(particles, factors)
enforceBounds(particles, factors)

Arguments

`particles`	The particle set
`factors`	the defined range for objective function parameters

Value

The particle inside the valid limits

es.evaluate

Description

For each element in solution 's' evaluate the respective fitness.

Usage

es.evaluate(f, s, enforce = TRUE)
es.evaluate(f, s, enforce = TRUE)

Arguments

`f`	A reference to an instance of objective function
`s`	The set of solutions
`enforce`	If true the values are enforced to fall within provided range

Value

The solution ordered by its fitness.

Estimates

Description

A simple class for encapsulating the return of metaheuristic methods

extremize

Description

Entry point for optimization functions

Usage

extremize(type, objective, options = NULL)
extremize(type, objective, options = NULL)

Arguments

`type`	The optimization method (aco,pso,saa,sda)
`objective`	An instance of ObjectiveFunction (or subclass) class ObjectiveFunction
`options`	An apropiate instance from a sublclass of Options class

Examples

## Not run: 
 f<- PlainFunction$new(f0.rosenbrock2)

 f$Parameter(name="x1",min=-100,max=100)
 f$Parameter(name="x2",min=-100,max=100)

 extremize("pso", f)

## End(Not run)

## Not run: 
 f<- PlainFunction$new(f0.rosenbrock2)

 f$Parameter(name="x1",min=-100,max=100)
 f$Parameter(name="x2",min=-100,max=100)

 extremize("pso", f)

## End(Not run)

f0.ackley

Description

The ackley function of N variables for testing optimization methods. The global optima for the function is given by xi = 0, forall i E 1...N, f(x) = 0. Domain xi E [-32.768, 32.768], for all i = 1, ..., d

Usage

f0.ackley(...)
f0.ackley(...)

Arguments

...

The variadic list of function variables.

Value

The function value

References

https://www.sfu.ca/~ssurjano/ackley.html

f0.ackley4

Description

The ackley function of four variables for testing optimization methods. The global optima for the function is given by xi = 0, forall i E 1...N, f(x) = 0.

Usage

f0.ackley4(x1, x2, x3, x4)
f0.ackley4(x1, x2, x3, x4)

Arguments

`x1`	The first function variable
`x2`	The second function variable
`x3`	The third function variable
`x4`	The fourth function variable

Value

The function value

f0.adtn.rosenbrock2

Description

Two variable Rosenbrock function with random additive noise.

Usage

f0.adtn.rosenbrock2(x1, x2)
f0.adtn.rosenbrock2(x1, x2)

Arguments

`x1`	Parameter 1
`x2`	Parameter 2

f0.bohachevsky

Description

The Bohachevsky function of N variables for testing optimization methods. The global optima for the function is given by xi = 0, forall i E 1...N, f(x) = 0.

Usage

f0.bohachevsky(...)
f0.bohachevsky(...)

Arguments

...

The variadic list of function variables.

Value

The function value

References

http://deap.gel.ulaval.ca/doc/dev/api/benchmarks.html

f0.bohachevsky4

Description

The Bohachevsky function of four variables for testing optimization methods. The global optima for the function is given by xi = 0, forall i E 1...N, f(x) = 0.

Usage

f0.bohachevsky4(x1, x2, x3, x4)
f0.bohachevsky4(x1, x2, x3, x4)

Arguments

`x1`	The first function variable
`x2`	The second function variable
`x3`	The third function variable
`x4`	The fourth function variable

Value

The function value

f0.cigar

Description

The Cigar function of N variables for testing optimization methods. The global optima for the function is given by xi = 0, forall i E 1...N, f(x) = 0.

Usage

f0.cigar(...)
f0.cigar(...)

Arguments

...

The variadic list of function variables.

Value

The function value

References

http://deap.gel.ulaval.ca/doc/dev/api/benchmarks.html

f0.cigar4

Description

The Cigar function of four variables for testing optimization methods. The global optima for the function is given by xi = 0, forall i E 1...N, f(x) = 0.

Usage

f0.cigar4(x1, x2, x3, x4)
f0.cigar4(x1, x2, x3, x4)

Arguments

`x1`	The first function variable
`x2`	The second function variable
`x3`	The third function variable
`x4`	The fourth function variable

Value

The function value

f0.griewank

Description

The griewank function of N variables for testing optimization methods. The global optima for the function is given by xi = 0, forall i E 1...N, f(x) = 0.

Usage

f0.griewank(...)
f0.griewank(...)

Arguments

...

The variadic list of function variables.

Value

The function value

References

http://deap.gel.ulaval.ca/doc/dev/api/benchmarks.html

f0.griewank4

Description

The griewank function of four variables for testing optimization methods. The global optima for the function is given by xi = 0, forall i E 1...N, f(x) = 0.

Usage

f0.griewank4(x1, x2, x3, x4)
f0.griewank4(x1, x2, x3, x4)

Arguments

`x1`	The first function variable
`x2`	The second function variable
`x3`	The third function variable
`x4`	The fourth function variable

Value

The function value

f0.nlnn.rosenbrock2

Description

Two variable Rosenbrock function with random additive noise.

Usage

f0.nlnn.rosenbrock2(x1, x2)
f0.nlnn.rosenbrock2(x1, x2)

Arguments

`x1`	Parameter 1
`x2`	Parameter 2

Period tuning for Predator-Prey base

Description

This function is an example on how EvoPER can be used for estimating the parameter values in order to produce oscilations with the desired period. It is not intended to be used directelly, the provided wrappers should be instead.

Usage

f0.periodtuningpp(x1, x2, x3, x4, period)
f0.periodtuningpp(x1, x2, x3, x4, period)

Arguments

`x1`	The growth rate of prey
`x2`	The decay rate of predator
`x3`	The predating effect on prey
`x4`	The predating effecto on predator
`period`	The desired oscilation period

Value

The solution fitness cost

Period tuning of 12 time units for Predator-Prey

Description

This function is an example on how EvoPER can be used for estimating the parameter values in order to produce oscilations with the desired period.

Usage

f0.periodtuningpp12(x1, x2, x3, x4)
f0.periodtuningpp12(x1, x2, x3, x4)

Arguments

`x1`	The growth rate of prey
`x2`	The decay rate of predator
`x3`	The predating effect on prey
`x4`	The predating effecto on predator

Value

The solution fitness cost

Examples

## Not run: 
rm(list=ls())
set.seed(-27262565)
f<- PlainFunction$new(f0.periodtuningpp12)
f$Parameter(name="x1",min=0.5,max=2)
f$Parameter(name="x2",min=0.5,max=2)
f$Parameter(name="x3",min=0.5,max=2)
f$Parameter(name="x4",min=0.5,max=2)
extremize("pso", f)

## End(Not run)

## Not run: 
rm(list=ls())
set.seed(-27262565)
f<- PlainFunction$new(f0.periodtuningpp12)
f$Parameter(name="x1",min=0.5,max=2)
f$Parameter(name="x2",min=0.5,max=2)
f$Parameter(name="x3",min=0.5,max=2)
f$Parameter(name="x4",min=0.5,max=2)
extremize("pso", f)

## End(Not run)

Period tuning of 24 time units for Predator-Prey

Description

This function is an example on how EvoPER can be used for estimating the parameter values in order to produce oscilations with the desired period.

Usage

f0.periodtuningpp24(x1, x2, x3, x4)
f0.periodtuningpp24(x1, x2, x3, x4)

Arguments

`x1`	The growth rate of prey
`x2`	The decay rate of predator
`x3`	The predating effect on prey
`x4`	The predating effecto on predator

Value

The solution fitness cost

Examples

## Not run: 
rm(list=ls())
set.seed(-27262565)
f<- PlainFunction$new(f0.periodtuningpp24)
f$Parameter(name="x1",min=0.5,max=2)
f$Parameter(name="x2",min=0.5,max=2)
f$Parameter(name="x3",min=0.5,max=2)
f$Parameter(name="x4",min=0.5,max=2)
extremize("pso", f)

## End(Not run)

## Not run: 
rm(list=ls())
set.seed(-27262565)
f<- PlainFunction$new(f0.periodtuningpp24)
f$Parameter(name="x1",min=0.5,max=2)
f$Parameter(name="x2",min=0.5,max=2)
f$Parameter(name="x3",min=0.5,max=2)
f$Parameter(name="x4",min=0.5,max=2)
extremize("pso", f)

## End(Not run)

Period tuning of 48 time units for Predator-Prey

Description

This function is an example on how EvoPER can be used for estimating the parameter values in order to produce oscilations with the desired period.

Usage

f0.periodtuningpp48(x1, x2, x3, x4)
f0.periodtuningpp48(x1, x2, x3, x4)

Arguments

`x1`	The growth rate of prey
`x2`	The decay rate of predator
`x3`	The predating effect on prey
`x4`	The predating effecto on predator

Value

The solution fitness cost

Examples

## Not run: 
rm(list=ls())
set.seed(-27262565)
f<- PlainFunction$new(f0.periodtuningpp24)
f$Parameter(name="x1",min=0.5,max=2)
f$Parameter(name="x2",min=0.5,max=2)
f$Parameter(name="x3",min=0.5,max=2)
f$Parameter(name="x4",min=0.5,max=2)
extremize("pso", f)

## End(Not run)

## Not run: 
rm(list=ls())
set.seed(-27262565)
f<- PlainFunction$new(f0.periodtuningpp24)
f$Parameter(name="x1",min=0.5,max=2)
f$Parameter(name="x2",min=0.5,max=2)
f$Parameter(name="x3",min=0.5,max=2)
f$Parameter(name="x4",min=0.5,max=2)
extremize("pso", f)

## End(Not run)

Period tuning of 72 time units for Predator-Prey

Description

This function is an example on how EvoPER can be used for estimating the parameter values in order to produce oscilations with the desired period.

Usage

f0.periodtuningpp72(x1, x2, x3, x4)
f0.periodtuningpp72(x1, x2, x3, x4)

Arguments

`x1`	The growth rate of prey
`x2`	The decay rate of predator
`x3`	The predating effect on prey
`x4`	The predating effecto on predator

Value

The solution fitness cost

Examples

## Not run: 
rm(list=ls())
set.seed(-27262565)
f<- PlainFunction$new(f0.periodtuningpp24)
f$Parameter(name="x1",min=0.5,max=2)
f$Parameter(name="x2",min=0.5,max=2)
f$Parameter(name="x3",min=0.5,max=2)
f$Parameter(name="x4",min=0.5,max=2)
extremize("pso", f)

## End(Not run)

## Not run: 
rm(list=ls())
set.seed(-27262565)
f<- PlainFunction$new(f0.periodtuningpp24)
f$Parameter(name="x1",min=0.5,max=2)
f$Parameter(name="x2",min=0.5,max=2)
f$Parameter(name="x3",min=0.5,max=2)
f$Parameter(name="x4",min=0.5,max=2)
extremize("pso", f)

## End(Not run)

f0.rosenbrock2

Description

Two variable Rosenbrock function, where f(1,1) = 0

Usage

f0.rosenbrock2(x1, x2)
f0.rosenbrock2(x1, x2)

Arguments

`x1`	Parameter 1
`x2`	Parameter 2

f0.rosenbrock4

Description

The rosenbrock function of 4 variables for testing optimization methods. The global optima for the function is given by xi = 1, forall i E 1...N, f(x) = 0.

Usage

f0.rosenbrock4(x1, x2, x3, x4)
f0.rosenbrock4(x1, x2, x3, x4)

Arguments

`x1`	The first function variable
`x2`	The second function variable
`x3`	The third function variable
`x4`	The fourth function variable

Value

The function value

f0.rosenbrockn

Description

The rosenbrock function of N variables for testing optimization methods. The global optima for the function is given by xi = 1, forall i E 1...N, f(x) = 0.

Usage

f0.rosenbrockn(...)
f0.rosenbrockn(...)

Arguments

...

The variadic list of function variables.

Value

The function value

References

http://deap.gel.ulaval.ca/doc/dev/api/benchmarks.html

f0.schaffer

Description

The schaffer function of N variables for testing optimization methods. The global optima for the function is given by xi = 0, forall i E 1...N, f(x) = 0.

Usage

f0.schaffer(...)
f0.schaffer(...)

Arguments

...

The variadic list of function variables.

Value

The function value

References

http://deap.gel.ulaval.ca/doc/dev/api/benchmarks.html

f0.schaffer4

Description

The Schaffer function of four variables for testing optimization methods. The global optima for the function is given by xi = 0, forall i E 1...N, f(x) = 0.

Usage

f0.schaffer4(x1, x2, x3, x4)
f0.schaffer4(x1, x2, x3, x4)

Arguments

`x1`	The first function variable
`x2`	The second function variable
`x3`	The third function variable
`x4`	The fourth function variable

Value

The function value

f0.schwefel

Description

The schwefel function of N variables for testing optimization methods. The global optima for the function is given by xi = 420.96874636, forall i E 1...N, f(x) = 0. The range of xi is [-500,500]

Usage

f0.schwefel(...)
f0.schwefel(...)

Arguments

...

The variadic list of function variables.

Value

The function value

References

http://deap.gel.ulaval.ca/doc/dev/api/benchmarks.html

f0.schwefel4

Description

The schwefel function of N variables for testing optimization methods. The global optima for the function is given by xi = 420.96874636, forall i E 1...N, f(x) = 0. The range of xi is [-500,500]

Usage

f0.schwefel4(x1, x2, x3, x4)
f0.schwefel4(x1, x2, x3, x4)

Arguments

`x1`	The first function variable
`x2`	The second function variable
`x3`	The third function variable
`x4`	The fourth function variable

Value

The function value

f0.test

Description

Simple test function f(1,2,3,4) = 0

Usage

f0.test(x1, x2, x3, x4)
f0.test(x1, x2, x3, x4)

Arguments

`x1`	Parameter 1
`x2`	Parameter 2
`x3`	Parameter 3
`x4`	Parameter 4

f1.ackley

Description

Usage

f1.ackley(x)
f1.ackley(x)

Arguments

`x`	The vector of function parameters

Value

The function value

References

https://www.sfu.ca/~ssurjano/ackley.html

f1.adtn.rosenbrock2

Description

Two variable Rosenbrock function with random additive noise.

Usage

f1.adtn.rosenbrock2(x)
f1.adtn.rosenbrock2(x)

Arguments

`x`	Parameter vector

f1.bohachevsky

Description

The Bohachevsky function of N variables for testing optimization methods. The global optima for the function is given by xi = 0, forall i E 1...N, f(x) = 0.

Usage

f1.bohachevsky(x)
f1.bohachevsky(x)

Arguments

`x`	The vector of function parameters

Value

The function value

References

http://deap.gel.ulaval.ca/doc/dev/api/benchmarks.html

f1.cigar

Description

The Cigar function of N variables for testing optimization methods. The global optima for the function is given by xi = 0, forall i E 1...N, f(x) = 0.

Usage

f1.cigar(x)
f1.cigar(x)

Arguments

`x`	The vector of function variables.

Value

The function value

References

http://deap.gel.ulaval.ca/doc/dev/api/benchmarks.html

f1.griewank

Description

The griewank function of N variables for testing optimization methods. The global optima for the function is given by xi = 0, forall i E 1...N, f(x) = 0.

Usage

f1.griewank(x)
f1.griewank(x)

Arguments

`x`	The vector of function parameters

Value

The function value

References

http://deap.gel.ulaval.ca/doc/dev/api/benchmarks.html

f1.nlnn.rosenbrock2

Description

Two variable Rosenbrock function with random additive noise.

Usage

f1.nlnn.rosenbrock2(x)
f1.nlnn.rosenbrock2(x)

Arguments

`x`	Parameter vector

f1.rosenbrock2

Description

Two variable Rosenbrock function, where f(c(1,1)) = 0

Usage

f1.rosenbrock2(x)
f1.rosenbrock2(x)

Arguments

`x`	Parameter vector

f1.rosenbrockn

Description

The rosenbrock function of N variables for testing optimization methods. The global optima for the function is given by xi = 1, forall i E 1...N, f(x) = 0.

Usage

f1.rosenbrockn(x)
f1.rosenbrockn(x)

Arguments

`x`	The vector of function parameters

Value

The function value

References

http://deap.gel.ulaval.ca/doc/dev/api/benchmarks.html

f1.schaffer

Description

The schaffer function of N variables for testing optimization methods. The global optima for the function is given by xi = 0, forall i E 1...N, f(x) = 0.

Usage

f1.schaffer(x)
f1.schaffer(x)

Arguments

`x`	The vector of function parameters

Value

The function value

References

http://deap.gel.ulaval.ca/doc/dev/api/benchmarks.html

f1.schwefel

Description

The schwefel function of N variables for testing optimization methods. The global optima for the function is given by xi = 420.96874636, forall i E 1...N, f(x) = 0. The range of xi is [-500,500]

Usage

f1.schwefel(x)
f1.schwefel(x)

Arguments

`x`	The vector of function variables.

Value

The function value

References

http://deap.gel.ulaval.ca/doc/dev/api/benchmarks.html

f1.test

Description

Simple test function f(c(1,2,3,4)) = 0

Usage

f1.test(x)
f1.test(x)

Arguments

`x`	Parameter vector

fixdfcolumns

Description

Coerce dataframe columns to a specic type.

Usage

fixdfcolumns(df, cols = c(), skip = TRUE, type = as.numeric)
fixdfcolumns(df, cols = c(), skip = TRUE, type = as.numeric)

Arguments

`df`	The data frame.
`cols`	The dataframe columns to be skiped or included.
`skip`	If TRUE the column names in 'cols' are skiped. When FALSE logic is inverted.
`type`	The type for which data frame columns must be converted.

Value

The data frame with converted column types.

generateSolution

Description

Generates a problema solution using discrete leves

Usage

generateSolution(parameters, size)
generateSolution(parameters, size)

Arguments

`parameters`	The Objective Function parameter list
`size`	The solution size

Value

The solution set

getFitness

Description

Given a set S of N solutions created with sortSolution, this function returns the solution component fot the best solution.

Usage

getFitness(S, i = NULL)
getFitness(S, i = NULL)

Arguments

`S`	The solution set
`i`	The fitness index, if null return the whole column.

Value

The selected fitness entry

getSolution

Description

Given a set S of N solutions created with sortSolution, this function returns the solution component. A solutions is a set of solutions and their associated fitness

Usage

getSolution(S)
getSolution(S)

Arguments

`S`	The solution set

Value

The solution set

gm.mean

Description

Simple implementation for geometric mean

Usage

gm.mean(x)
gm.mean(x)

Arguments

x

data

Value

geometric mean for data

gm.sd

Description

Simple implementation for geometric standard deviation

Usage

gm.sd(x, mu = NULL)
gm.sd(x, mu = NULL)

Arguments

`x`	data
`mu`	The geometric mean. If not provided it is calculated.

Value

geometric standard deviation for data

histplothelper

Description

Simple helper for ploting histograms

Usage

histplothelper(d, x, title = NULL)
histplothelper(d, x, title = NULL)

Arguments

`d`	A data frame.
`x`	A string with the dataframe column name for histogram
`title`	The plot title

Value

A ggplot2 plot object

initSolution

Description

Creates the initial Solution population taking into account the lower an upper bounds of provided experiment factors.

Usage

initSolution(parameters, N = 20, sampling = "mcs")
initSolution(parameters, N = 20, sampling = "mcs")

Arguments

`parameters`	The Objective Function parameter list
`N`	The size of Solution population
`sampling`	The population sampling scheme, namelly <mcs\|lhs\|ffs> standing respectively for montecarlo sampling, latin hypercube sampling and full factorial sampling

Value

A random set of solutions

lowerBound

Description

Checks if parameters is greater than the lower bounds

Usage

lowerBound(particles, factors)
lowerBound(particles, factors)

Arguments

`particles`	The particle set
`factors`	the defined range for objective function parameters

Value

The particle greater than or equal to lower limit

Magnitude

Description

Calculates the magnitude order for a given value

Usage

Magnitude(v)
Magnitude(v)

Arguments

`v`	The numerical value

Value

The magnitude order

naiveperiod

Description

A naive approach for finding the period in a series of data points

Usage

naiveperiod(d)
naiveperiod(d)

Arguments

`d`	The data to search period

Value

A list with the average period and amplitude

NetLogoFunction

Description

NetLogoFunction class

NLWrapper.FindJar

Description

Search for the netlogo jar file on the provided path

Usage

NLWrapper.FindJar(path)
NLWrapper.FindJar(path)

Arguments

path

The base path for searching

Value

The path for NetLogo jar file

NLWrapper.GetParameter

Description

Gets the value of a model parameter

Usage

NLWrapper.GetParameter(obj, name)
NLWrapper.GetParameter(obj, name)

Arguments

`obj`	The object retuned by NLWrapper.Model
`name`	The parameter name string or the collection of parameter names

Value

The parameter values

Examples

## Not run: 
   rm(list=ls())
   p<- "C:/Program Files/NetLogo 6.0.4/app"
   m<- file.path(nlpath, "models", "Sample Models", "Biology", "Wolf Sheep Predation.nlogo")
   o<- NLWrapper.Model(p, m)
   v<- NLWrapper.GetParameter(o, c("initial-number-sheep"))

   or

   v<- NLWrapper.GetParameter(o, c("initial-number-sheep","initial-number-wolves")))

## End(Not run)

## Not run: 
   rm(list=ls())
   p<- "C:/Program Files/NetLogo 6.0.4/app"
   m<- file.path(nlpath, "models", "Sample Models", "Biology", "Wolf Sheep Predation.nlogo")
   o<- NLWrapper.Model(p, m)
   v<- NLWrapper.GetParameter(o, c("initial-number-sheep"))

   or

   v<- NLWrapper.GetParameter(o, c("initial-number-sheep","initial-number-wolves")))

## End(Not run)

NLWrapper.Model

Description

This wrapper prepares the environment and instantiates the model

Usage

NLWrapper.Model(netlogodir, modelfile, dataset, maxtime)
NLWrapper.Model(netlogodir, modelfile, dataset, maxtime)

Arguments

`netlogodir`	The base path of NetLogo installation
`modelfile`	The absolute path for NetLogo model file
`dataset`	The names of model variables
`maxtime`	The total number of iterations

Examples

## Not run: 
   rm(list=ls())
   p<- "C:/Program Files/NetLogo 6.0.4/app"
   output<- c("count sheep", "count wolves")
   m<- file.path(nlpath, "models", "Sample Models", "Biology", model, "Wolf Sheep Predation.nlogo")
   o<- NLWrapper.Model(p, m, output, 150)

## End(Not run)

## Not run: 
   rm(list=ls())
   p<- "C:/Program Files/NetLogo 6.0.4/app"
   output<- c("count sheep", "count wolves")
   m<- file.path(nlpath, "models", "Sample Models", "Biology", model, "Wolf Sheep Predation.nlogo")
   o<- NLWrapper.Model(p, m, output, 150)

## End(Not run)

NLWrapper.Run

Description

Executes a NetLogo Model using rNetLogo

Usage

NLWrapper.Run(obj, r = 1, seed = c())
NLWrapper.Run(obj, r = 1, seed = c())

Arguments

`obj`	The object retuned by NLWrapper.Model
`r`	The number of replications
`seed`	The collection of random seeds

NLWrapper.RunExperiment

Description

Executes a NetLogo Model using rNetLogo

Usage

NLWrapper.RunExperiment(obj, r = 1, design, FUN)
NLWrapper.RunExperiment(obj, r = 1, design, FUN)

Arguments

`obj`	The object retuned by NLWrapper.Model
`r`	The number of replications
`design`	The desing matrix holding parameter sampling
`FUN`	THe calibration function.

Value

A list containing the the parameters, the calibration functio output and the whole resultset

Examples

## Not run: 
   rm(list=ls())
   objectivefn<- function(params, results) { 0 }

   f<- AddFactor(name="initial-number-sheep",min=100,max=250)
   f<- AddFactor(factors=f, name="initial-number-wolves",min=50,max=150)
   f<- AddFactor(factors=f, name="grass-regrowth-time",min=30,max=100)
   f<- AddFactor(factors=f, name="sheep-gain-from-food",min=1,max=50)
   f<- AddFactor(factors=f, name="wolf-gain-from-food",min=1,max=100)
   f<- AddFactor(factors=f, name="sheep-reproduce",min=1,max=20)
   f<- AddFactor(factors=f, name="wolf-reproduce",min=1,max=20)

   design<- AoE.LatinHypercube(factors=f)

   p<- "C:/Program Files/NetLogo 6.0.4/app"
   m<- file.path(p, "models", "Sample Models", "Biology", "Wolf Sheep Predation.nlogo")
   output<- c("count sheep", "count wolves")
   o<- NLWrapper.Model(p, m, output, 150)
   v<- RunExperiment(o, r=1, design, objectivefn)
   NLWrapper.Shutdown(o)

## End(Not run)

## Not run: 
   rm(list=ls())
   objectivefn<- function(params, results) { 0 }

   f<- AddFactor(name="initial-number-sheep",min=100,max=250)
   f<- AddFactor(factors=f, name="initial-number-wolves",min=50,max=150)
   f<- AddFactor(factors=f, name="grass-regrowth-time",min=30,max=100)
   f<- AddFactor(factors=f, name="sheep-gain-from-food",min=1,max=50)
   f<- AddFactor(factors=f, name="wolf-gain-from-food",min=1,max=100)
   f<- AddFactor(factors=f, name="sheep-reproduce",min=1,max=20)
   f<- AddFactor(factors=f, name="wolf-reproduce",min=1,max=20)

   design<- AoE.LatinHypercube(factors=f)

   p<- "C:/Program Files/NetLogo 6.0.4/app"
   m<- file.path(p, "models", "Sample Models", "Biology", "Wolf Sheep Predation.nlogo")
   output<- c("count sheep", "count wolves")
   o<- NLWrapper.Model(p, m, output, 150)
   v<- RunExperiment(o, r=1, design, objectivefn)
   NLWrapper.Shutdown(o)

## End(Not run)

NLWrapper.SetParameter

Description

Set parameter values

Usage

NLWrapper.SetParameter(obj, parameters)
NLWrapper.SetParameter(obj, parameters)

Arguments

`obj`	The object retuned by NLWrapper.Model
`parameters`	The data frame containing the paramters

Examples

## Not run: 
   rm(list=ls())
   p<- "C:/Program Files/NetLogo 6.0.4/app"
   m<- file.path(nlpath, "models", "Sample Models", "Biology", "Wolf Sheep Predation.nlogo")
   o<- NLWrapper.Model(p, m)

## End(Not run)

## Not run: 
   rm(list=ls())
   p<- "C:/Program Files/NetLogo 6.0.4/app"
   m<- file.path(nlpath, "models", "Sample Models", "Biology", "Wolf Sheep Predation.nlogo")
   o<- NLWrapper.Model(p, m)

## End(Not run)

NLWrapper.SetRandomSeed

Description

Configures the random seed

Usage

NLWrapper.SetRandomSeed(obj, seed)
NLWrapper.SetRandomSeed(obj, seed)

Arguments

`obj`	The object retuned by NLWrapper.Model
`seed`	The new random seed

NLWrapper.Shutdown

Description

This wrapper terminates RNetLogo execution environment

Usage

NLWrapper.Shutdown(obj)
NLWrapper.Shutdown(obj)

Arguments

obj

The object retuned by NLWrapper.Model

ObjectiveFunction class

Description

The base class for optimization functions.

Fields

object: The raw output of objective function
objective: The objective function
parameters: The parameter list for objective function
value: The results from objective function

Options

Description

The base class for the options for the optimization metaheuristics

Fields

type: The configuration type
neighborhood: The neighborhood function for population methods
discrete: Flag indicating that and specific algorithm is discrete or continuous
nlevelz: Default value for generating parameter levels when range is provided, default value is 5
container: The object holding the configuration otions

OptionsACOR

Description

Options for ACOR method

OptionsEES1

Description

Options for EvoPER Evolutionary Stratety 1

OptionsEES2

Description

Options for Serial Dilutions method

Fields

dilutions: The desired dilutions

OptionsFactory

Description

Instantiate the Options class required for the specific metaheuristic method.

Usage

OptionsFactory(type, v = NULL)
OptionsFactory(type, v = NULL)

Arguments

`type`	The metaheuristic method
`v`	The options object

Value

Options object

OptionsPSO

Description

Options for PSO optimization metaheuristic

OptionsSAA

Description

Options for SAA method

Fields

temperature: The temperature dacay function

OptionsTS

Description

Options for Tabu search optimization metaheuristic

paramconverter

Description

Convert parameter from continuous to discrete and vice-versa if needed

Usage

paramconverter(parameters, discrete, levelz = 5)
paramconverter(parameters, discrete, levelz = 5)

Arguments

`parameters`	The current parameter set
`discrete`	The desired parameter type
`levelz`	When discrete is true the number of levels to be generated

Value

The parameter collection casted to desired mode

partSolutionSpace

Description

Creates the initial Solution population taking into account the lower an upper bounds of provided experiment factors. This method works by dividing the solution space into partitions of size 'd' and then creating a full factorial combination of partitions.

Usage

partSolutionSpace(parameters, d = 4)
partSolutionSpace(parameters, d = 4)

Arguments

`parameters`	The Objective Function parameter list
`d`	The partition size. Default value 4.

Value

A set of solutions

PlainFunction

Description

PlainFunction Class

pop.first

Description

pop an element

Usage

pop.first(x)
pop.first(x)

Arguments

`x`	The element collection

Value

The first element added to list FIFO

pop.last

Description

pop an element

Usage

pop.last(x)
pop.last(x)

Arguments

`x`	The element collection

Value

The last element added to list LIFO

predatorprey

Description

The solver for Lotka-Volterra differential equation.

Usage

predatorprey(x1, x2, x3, x4)
predatorprey(x1, x2, x3, x4)

Arguments

`x1`	The growth rate of prey
`x2`	The decay rate of predator
`x3`	The predating effect on prey
`x4`	The predating effecto on predator

Value

The ODE solution

predatorprey.plot0

Description

Generate a plot for the predator-prey ODE output.

Usage

predatorprey.plot0(x1, x2, x3, x4, title = NULL)
predatorprey.plot0(x1, x2, x3, x4, title = NULL)

Arguments

`x1`	The growth rate of prey
`x2`	The decay rate of predator
`x3`	The predating effect on prey
`x4`	The predating effect on predator
`title`	The optional plot title. May be omited.

Value

An ggplot2 object

Examples

## Not run: 
 predatorprey.plot0(1.351888, 1.439185, 1.337083, 0.9079049)

## End(Not run)

## Not run: 
 predatorprey.plot0(1.351888, 1.439185, 1.337083, 0.9079049)

## End(Not run)

predatorprey.plot1

Description

Simple wrapper for 'predatorprey.plot0' accepting the parameters as a list.

Usage

predatorprey.plot1(x, title = NULL)
predatorprey.plot1(x, title = NULL)

Arguments

`x`	A list containing the values of predator/prey parameters c1, c2, c3 and c4 denoting respectivelly the growth rate of prey, the decay rate of predator, the predating effect on prey and the predating effect on predator
`title`	The optional plot title. May be omited.

Value

An ggplot2 object

Examples

## Not run: 
 rm(list=ls())
 predatorprey.plot1(v$getBest()[1:4])

## End(Not run)

## Not run: 
 rm(list=ls())
 predatorprey.plot1(v$getBest()[1:4])

## End(Not run)

pso.best

Description

Search for the best particle solution which minimize the objective function.

Usage

pso.best(objective, particles)
pso.best(objective, particles)

Arguments

`objective`	The results of evaluating the objective function
`particles`	The particles tested

Value

The best particle

pso.chi

Description

Implementation of constriction coefficient

Usage

pso.chi(phi1, phi2)
pso.chi(phi1, phi2)

Arguments

`phi1`	Acceleration coefficient toward the previous best
`phi2`	Acceleration coefficient toward the global best

Value

The calculated constriction coefficient

pso.lbest

Description

Finds the lbest for the particle 'i' using the topology function given by the topology parameter.

Usage

pso.lbest(i, pbest, topology)
pso.lbest(i, pbest, topology)

Arguments

`i`	The particle position
`pbest`	The pbest particle collection
`topology`	The desired topology function

Value

The lbes for i th particle

pso.neighborhood.K2

Description

The neighborhood function for a simple linear topology where every particle has k = 2 neighbors

Usage

pso.neighborhood.K2(i, n)
pso.neighborhood.K2(i, n)

Arguments

`i`	The particle position
`n`	the size of particle population

pso.neighborhood.K4

Description

The von neumann neighborhood function for a lattice-based topology where every particle has k = 4 neighbors

Usage

pso.neighborhood.K4(i, n)
pso.neighborhood.K4(i, n)

Arguments

`i`	The particle position
`n`	the size of particle population

pso.neighborhood.KN

Description

Simple helper method for 'gbest' neighborhood

Usage

pso.neighborhood.KN(i, n)
pso.neighborhood.KN(i, n)

Arguments

`i`	The particle position
`n`	the size of particle population

pso.printbest

Description

Shows the best particle of each of simulated generations

Usage

pso.printbest(objective, particles, generation, title)
pso.printbest(objective, particles, generation, title)

Arguments

`objective`	An instance of ObjectiveFunction (or subclass) class ObjectiveFunction
`particles`	The current particle population
`generation`	The current generation
`title`	Some informational text to be shown

pso.velocity

Description

Calculates the PSO Velocity

Usage

pso.Velocity(W = 1, Vi, phi1, phi2, Pi, Pg, Xi)
pso.Velocity(W = 1, Vi, phi1, phi2, Pi, Pg, Xi)

Arguments

`W`	Weight (Inertia weight or constriction coefficient)
`Vi`	Current Velocity vector
`phi1`	Acceleration coefficient toward the previous best
`phi2`	Acceleration coefficient toward the global best
`Pi`	Personal best
`Pg`	Neighborhood best
`Xi`	Particle vector

Value

Updated velocity

push

Description

push an element

Usage

push(x, v)
push(x, v)

Arguments

`x`	The collection of elements
`v`	The value to be pushed

Value

The collection of elements

random.whell

Description

A simple randon seed generator

Usage

random.wheel()
random.wheel()

Value

A random number for seeding

RepastFunction

Description

RepastFunction class

saa.bolt

Description

Temperature function boltzmann

Usage

saa.bolt(t0, k)
saa.bolt(t0, k)

Arguments

`t0`	The current temperature
`k`	The annealing value

Value

The new temperature

saa.neighborhood

Description

Generates neighbor solutions for simulated annealing

Usage

saa.neighborhood(f, S, d, n)
saa.neighborhood(f, S, d, n)

Arguments

`f`	An instance of ObjectiveFunction (or subclass) class ObjectiveFunction
`S`	The current solution to find a neighbor
`d`	The distance from current solution S `distance = (max - min) * d`
`n`	The number of parameters to be perturbed

Value

The neighbor of solution S

saa.neighborhood1

Description

Generates neighbor solutions perturbing one parameter from current solution S picked randonly.

Usage

saa.neighborhood1(f, S, d)
saa.neighborhood1(f, S, d)

Arguments

`f`	An instance of ObjectiveFunction (or subclass) class ObjectiveFunction
`S`	The current solution to find a neighbor
`d`	The distance from current solution S `distance = (max - min) * d`

Value

The neighbor of solution of S

saa.neighborhoodH

Description

Generates neighbor solutions perturbing half parameters from current solution S.

Usage

saa.neighborhoodH(f, S, d)
saa.neighborhoodH(f, S, d)

Arguments

`f`	An instance of ObjectiveFunction (or subclass) class ObjectiveFunction
`S`	The current solution to find a neighbor
`d`	The distance from current solution S `distance = (max - min) * d`

Value

The neighbor of solution of S

saa.neighborhoodN

Description

Generates neighbor solutions perturbing all parameters from current solution S.

Usage

saa.neighborhoodN(f, S, d)
saa.neighborhoodN(f, S, d)

Arguments

`f`	An instance of ObjectiveFunction (or subclass) class ObjectiveFunction
`S`	The current solution to find a neighbor
`d`	The distance from current solution S `distance = (max - min) * d`

Value

The neighbor of solution of S

saa.tbyk

Description

Temperature function t/k

Usage

saa.tbyk(t0, k)
saa.tbyk(t0, k)

Arguments

`t0`	The current temperature
`k`	The annealing value

Value

The new temperature

saa.tcte

Description

Temperature function cte * t0

Usage

saa.tcte(t0, k)
saa.tcte(t0, k)

Arguments

`t0`	The current temperature
`k`	The annealing value

Value

The new temperature

saa.texp

Description

Temperature function exponential

Usage

saa.texp(t0, k)
saa.texp(t0, k)

Arguments

`t0`	The current temperature
`k`	The annealing value

Value

The new temperature

scatterplotlothelper

Description

Simple helper for ploting 3d scaterplots

Usage

scatterplotlothelper(d, x, y, z, title = NULL)
scatterplotlothelper(d, x, y, z, title = NULL)

Arguments

`d`	A data frame.
`x`	A string with the dataframe column name for x axis
`y`	A string with the dataframe column name for y axis
`z`	A string with the dataframe column name for z axis
`title`	The optional plot title. May be omited.

Value

A scatter3D plot

searchrow

Description

Search for a value value on a matrix

Usage

searchrow(ddata, value)
searchrow(ddata, value)

Arguments

`ddata`	The matrix containing the dataset
`value`	The value to search for

Value

Boolean TRUE for those indexes matching value

show.comp1

Description

Generates a barplot comparing the number of evalutions for algorithms ("saa","pso","acor","ees1").

Usage

show.comp1(mydata, what, title = NULL)
show.comp1(mydata, what, title = NULL)

Arguments

`mydata`	The data generated with 'summarize.comp1'
`what`	The name of variable to plot on 'y' axis
`title`	the plot title

Examples

## Not run: 
	p.a<- show.comp1(d.cigar4,"evals","(a) Cigar function")
	p.b<- show.comp1(d.schaffer4,"evals","(b) Schafer function")
	p.c<- show.comp1(d.griewank4,"evals","(c) Griewank function")
	p.d<- show.comp1(d.bohachevsky4,"evals","(d) Bohachevsky function")

## End(Not run)

## Not run: 
	p.a<- show.comp1(d.cigar4,"evals","(a) Cigar function")
	p.b<- show.comp1(d.schaffer4,"evals","(b) Schafer function")
	p.c<- show.comp1(d.griewank4,"evals","(c) Griewank function")
	p.d<- show.comp1(d.bohachevsky4,"evals","(d) Bohachevsky function")

## End(Not run)

slope

Description

Simple function for calculate the slope on the ith element position

Usage

slope(x, y, i)
slope(x, y, i)

Arguments

`x`	The x vector
`y`	The y vector
`i`	The position

Value

The slope

slopes

Description

Calcule all slopes for the discrete x,y series

Usage

slopes(x, y)
slopes(x, y)

Arguments

`x`	The x vector
`y`	The y vector

Value

A vector with all slopes

sortSolution

Description

Sort solution by its respective fitness

Usage

sortSolution(s, f)
sortSolution(s, f)

Arguments

`s`	Problem solution
`f`	The function evaluation for s

summarize.comp1

Description

Provides as summary with averged values of experimental setup

Usage

summarize.comp1(mydata)
summarize.comp1(mydata)

Arguments

mydata

The data frame generated with 'compare.algorithms1'

Value

The summarized data

tabu.getNeighbors

Description

create neighbor solutions

Usage

tabu.getNeighbors(tabu, parameters, solution, size)
tabu.getNeighbors(tabu, parameters, solution, size)

Arguments

`tabu`	The tabu list
`parameters`	The parameter set
`solution`	The current solution
`size`	The neigborhood size

Value

The neighbor for solution

tabu.istabu

Description

Check whether a solution is present on tabulist

Usage

tabu.istabu(tabulist, solution)
tabu.istabu(tabulist, solution)

Arguments

`tabulist`	The matrix of tabu solutions
`solution`	The solution value to be checked

Value

Boolean TRUE tabulist contains the solution

upperBound

Description

Checks if parameters is below the upper bounds

Usage

upperBound(particles, factors)
upperBound(particles, factors)

Arguments

`particles`	The particle set
`factors`	the defined range for objective function parameters

Value

The particle inside the valid upper bound

xmeanci1

Description

Calculates confidence interval of mean for provided data with desired confidence level. This functions uses bootstrap resampling scheme for estimanting the CI.

Usage

xmeanci1(x, alpha = 0.95)
xmeanci1(x, alpha = 0.95)

Arguments

`x`	The data set for which CI will be calculated
`alpha`	The confidence level. The default value is 0.95 (95%)

Value

The confidence interval for the mean calculated using 'boot.ci'

xmeanci2

Description

Calculates confidence interval of mean for provided data with desired confidence level.

Usage

xmeanci2(x, alpha = 0.95)
xmeanci2(x, alpha = 0.95)

Arguments

`x`	The data set for which CI will be calculated
`alpha`	The confidence level. The default value is 0.95 (95%)

Value

The confidence interval for the mean

xyplothelper

Description

Simple helper for ploting xy dispersion points.

Usage

xyplothelper(d, x, y, title = NULL)
xyplothelper(d, x, y, title = NULL)

Arguments

`d`	A data frame.
`x`	A string with the dataframe column name for x axis
`y`	A string with the dataframe column name for y axis
`title`	The optional plot title. May be omited.

Value

A ggplot2 plot object

Package 'evoper'

Help Index

Ant colony optimization for continuous domains

Description

Usage

Arguments

References

Examples

EvoPER Evolutionary Strategy 1

Description

Usage

Arguments

Examples

EvoPER Evolutionary Strategy 2

Description

Usage

Arguments

Examples

abm.pso

Description

Usage

Arguments

References

Examples

abm.saa

Description

Usage

Arguments

Value

References

Examples

Tabu Search metaheuristic

Description

Usage

Arguments

References

Examples

acor.archive

Description

Usage

Arguments

Value

References

acor.F

Description

Usage

Arguments

Value

Select the lth gaussian function

Description

Usage

Arguments

Value

References

acor.N

Description

Usage

Arguments

Value

Gaussian kernel choosing probability

Description

Usage

Arguments

Value

References

acor.S

Description

Usage

Arguments

Value

Sigma calculation for ACOr

Description

Usage

Arguments

Value

References

acor.updateants

Description

Usage

Arguments