This is my code i don't understand the error message:

library(rgenoud)
rm(list=ls())
set.seed(666)
#########################################################
# As a first step, it is assumed that all input parameters are independent of ageing :
#########################################################
InputDim <-20
# Max number of ageings in the inputs 
CPIRate <- rep(0.02 , InputDim )
# CPI inflation rate
LossInflaRate <- rep(0.02 , InputDim)
# inflation rate of losses
RiskFreeRate <- rep(0.04, InputDim )
# vector of risk free rate
RiskRate <- rep(0.06, InputDim )
# vector of risk free rate
################   lapse param begin :
LapseType <- rep("linear" , InputDim )
# type of the function used to calculate lapse rate= "power", "logistic", "linear"
CentralLapseRate <- rep(0.13 , InputDim )
# lapse rate for price factor = 1
ElasticityLeft  <- rep( 10, InputDim )
# left elasticity for price factor = 1
ElasticityRight  <- rep(10, InputDim )
# right elasticity for price factor = 1
WindowLeft <-  rep( 1 , InputDim )
# friction window price factor left bound
WindowRight <- rep( 1, InputDim )
# friction window price factor right bound
MinLapseRate <- rep( 0 , InputDim )
# min lapse rate 
MaxLapseRate <- rep( 1 , InputDim )
# min lapse rate
Alpha <- 0.5
#acquisition operator
###############  lapse param end
Price <- rep(1 , InputDim )
# Axa price for central market position and price factor = 1 
IndivMeanLossAmount <- rep(0.97 , InputDim )
# mean individual loss amount 
Commission <- rep(0 , InputDim )
# commision rate
IndividualExp <- rep(0 , InputDim )
# expense amount per risk
#######################################
# Market Expert Model parameter begin :
#######################################
LRUpBound <-  1.01349495023810
# Upper bound of the loss ratio
LRLowBound <-  0.743806254238095
# Lower bound of the loss ratio
TransitCoefMean <-  1.03001118178481
# Mean of the coefficient of transition
TransitCoefStdDev <-  0.0187801387384363
# Standard deviation of the coefficient of transition
#######################################
# Optimization algorithm & Monte-Carlo parameters :
#######################################
LTMeanComputYearNb <- 1000 
# Number of year used to simulate LR
NbOfSimScenar <-  1000
#  Number Monte-Carlo scenarios  
GenAlgoIterNb <- 100
# number of iteration for genetic algorithm
#######################################
# Problem size parameters :
#######################################
FirstYear <-2009
# first year of renewal
LastYear <- 2018
# last year of projected renewal
######################################
#  Function giving the minimum x (price factor = 1+x ) :
######################################
Xmin <-  function(LapseType, ElasticityLeft, WindowLeft, MinLapseRate ,CentralLapseRate)
{
InputDim <- length(LapseType)
x=numeric(InputDim)
for (i in 1:InputDim)
     {
     if  (LapseType[i]=="power" ) 
      x[i] <-  WindowLeft [i]*(MinLapseRate[i] /CentralLapseRate[i])^(1/ElasticityLeft[i])-1
    else  if (LapseType[i]=="linear")
      x [i]<- (WindowLeft[i]/ElasticityLeft[i])*(ElasticityLeft[i]-1+MinLapseRate[i] /CentralLapseRate[i])-1
     }
x
}      
 
######################################
#  Function giving the maximum x (price factor = 1+x ) :
######################################  
Xmax <-  function(LapseType, ElasticityLeft, WindowLeft, MaxLapseRate ,CentralLapseRate)
{
InputDim <- length(LapseType)
x=numeric(InputDim)
for (i in 1:InputDim)
  {
   if (LapseType[i]=="power" )
    x[i] <-  WindowLeft[i] *(MaxLapseRate[i]/CentralLapseRate[i])^(1/ElasticityLeft[i])-1
  else  if (LapseType[i]=="linear")
    x[i] <- (WindowLeft[i]/ElasticityLeft[i])*(ElasticityLeft[i]-1+MaxLapseRate[i]/CentralLapseRate[i])-1
     }
x
}
   
##########################################################
# Function simulating a scenario of projected market loss ratio :
##########################################################
LRScenar  <-  function(PriorLR,Phase,LRUpBound ,LRLowBound,TransitCoefMean,TransitCoefStdDev,LTMeanComputYearNb)
{
 initial  <- PriorLR                      
 LRScenar  <-  NULL
 phase2  <-  Phase
 SigmaLog  <-  (log(1+(TransitCoefStdDev /TransitCoefMean)^2))^0.5
 MuLog  <-  log(TransitCoefMean)-SigmaLog^2/2
 for (i in 1:LTMeanComputYearNb)
 {
 kk <- rlnorm(1, meanlog = MuLog, sdlog = SigmaLog)
 if (phase2!=1) kk  <-  (1/kk)
 x_1  <-  initial*kk
 initial  <-  x_1
 if (((x_1 > LRUpBound )&(phase2==1))|((x_1<LRLowBound)&(phase2==-1)))  phase2 <- (-phase2)       
 LRScenar <-  c(LRScenar,x_1)
 }
LRScenar
}
################################################
#  Function giving the lapse rate :
################################################
LapseRate <- function(x, LapseType,CentralLapseRate,
        ElasticityLeft, ElasticityRight,WindowLeft, WindowRight,
        MinLapseRate,MaxLapseRate )
{
PriceFactor <- 1+x
if (LapseType=="power")
 {
 if(PriceFactor<WindowLeft)
  r <- CentralLapseRate*(PriceFactor/WindowLeft)^ElasticityLeft
 else if((PriceFactor<=WindowRight)&(PriceFactor>=WindowLeft ))
  r <- CentralLapseRate
 else
  r <-CentralLapseRate*(PriceFactor/WindowRight)^ElasticityRight 
 }
else if (LapseType=="linear") 
 {
 if(PriceFactor<WindowLeft)
  r <- CentralLapseRate*(1+ElasticityLeft/WindowLeft*(PriceFactor-WindowLeft))
 else if((PriceFactor<=WindowRight)&(PriceFactor>=WindowLeft ))
  r  <- CentralLapseRate
 else
  r  <- CentralLapseRate*(1+ElasticityRight/WindowRight*(PriceFactor-WindowRight))
 }
min( max( r, MinLapseRate ) , MaxLapseRate)
}  
  
######################################################
#  Function calculating  a matrix of market price (excluding inflation) scenarios :
######################################################
MarketPriceMatrix <- function(NbOfFutureSimYear ,NbOfSimScenar,Phase,LRUpBound ,LRLowBound,TransitCoefMean,
        TransitCoefStdDev,PriorMarketPricePosition,MarketPricePhase,MarketPriceLTMean)
{
LRPhase <-  -MarketPricePhase
MarketPriceMatrix <-  matrix(numeric(NbOfFutureSimYear*NbOfSimScenar), nrow=NbOfSimScenar,ncol=NbOfFutureSimYear)
PriorLR <- 1 / (MarketPriceLTMean * PriorMarketPricePosition)
for (i in 1:NbOfSimScenar)
    MarketPriceMatrix[i,] <-  1/ LRScenar (PriorLR,LRPhase,LRUpBound ,LRLowBound,TransitCoefMean,
                  TransitCoefStdDev,NbOfFutureSimYear)/MarketPriceLTMean
MarketPriceMatrix
}
#############################################
#  Function calculating a scenario of results :
#############################################
ResultScenar <- function(FirstYear,OptimYear,Price,CumulRetentionCoef,Commission,IndividualExp,NbOfFutureSimYear, 
      IndivMeanLossAmount,CPIRate,LossInflaRate,Alpha)
{
CumulLossInflaRate <- cumprod(1+LossInflaRate)
CumulCPIRate <- cumprod(1+CPIRate)
y <- numeric(NbOfFutureSimYear)
delta <- OptimYear - FirstYear
if (OptimYear==FirstYear)
 {
y[1] <CumulRetentionCoef[1]*( (Price[1]*(1-Commission[delta +1])-IndivMeanLossAmount[delta +1])*CumulLossInflaRate[delta +1]
               - IndividualExp[delta +1]*CumulCPIRate[delta +1])   
  }             
for (i in 2: NbOfFutureSimYear)
 {
  k <-  delta + i
  y[i] <- (1-Alpha)*CumulRetentionCoef[i-1]*( (Price[i-1]*(1-Commission[k-1])-IndivMeanLossAmount[k-1])*CumulLossInflaRate[k-1] 
             - IndividualExp[k]*CumulCPIRate[k-1] )
              + Alpha*CumulRetentionCoef[i]*( (Price[i]*(1-Commission[k])-IndivMeanLossAmount[k])*CumulLossInflaRate[k] 
              - IndividualExp[k]*CumulCPIRate[k] ) 
 }
if  (OptimYear!=FirstYear)
{   
for (i in 1: (NbOfFutureSimYear+1))
 {
  k <-  delta + i
  y[i] <- (1-Alpha)*CumulRetentionCoef[i]*( (Price[i]*(1-Commission[k-1])-IndivMeanLossAmount[k-1])*CumulLossInflaRate[k-1] 
             - IndividualExp[k]*CumulCPIRate[k-1] )
              + Alpha*CumulRetentionCoef[i+1]*( (Price[i+1]*(1-Commission[k])-IndivMeanLossAmount[k])*CumulLossInflaRate[k]
              - IndividualExp[k]*CumulCPIRate[k] )
   }
}
y
}
##############################################
###  Function calculating a scenario of results :
###############################################
ResultActuSum <- function(FirstYear,OptimYear,x, NbOfFutureSimYear,
        CentralLapseRate, ElasticityLeft,WindowLeft, WindowRight, ElasticityRight,
          MarketPriceMean,Price,Commission,IndividualExp,IndivMeanLossAmount, 
          CPIRate,LossInflaRate,Beta,Alpha)
{
R <- numeric(NbOfFutureSimYear+1)
Price2 <- numeric(NbOfFutureSimYear+1)
CumulRetentionCoef <- numeric(NbOfFutureSimYear+1)
Result <- numeric(NbOfFutureSimYear+1)
delta <- OptimYear - FirstYear
for (i in 1:(NbOfFutureSimYear+1))
{
 k <-  delta + i
    R[i] <- LapseRate(x[i], LapseType[k],CentralLapseRate[k],
        ElasticityLeft[k], ElasticityRight[k],WindowLeft[k], WindowRight[k],
        MinLapseRate[k],MaxLapseRate[k] )
    Price2[i] <- Price[k]*(1+x[i])*MarketPriceMean[i]
 }
CumulRetentionCoef <-  cumprod(1-R)
Result <- ResultScenar(FirstYear,OptimYear,Price2,CumulRetentionCoef,Commission,IndividualExp,NbOfFutureSimYear, IndivMeanLossAmount,
        CPIRate,LossInflaRate,Alpha)
sum(Beta[1:NbOfFutureSimYear]*Result)
}

#########################################################
#  Function calculating the optimal poistioning for a given problem year & filtration :
#########################################################
YearFiltrationPbOptimPosition <- function( PriorMarketPricePosition,MarketPricePhase,LRUpBound,LRLowBound,TransitCoefMean,TransitCoefStdDev,
      LTMeanComputYearNb ,
      FirstYear,LastYear,OptimYear,
      CentralLapseRate, ElasticityLeft,WindowLeft, WindowRight, ElasticityRight,
        Price,Commission,IndividualExp,IndivMeanLossAmount,
        CPIRate,LossInflaRate,RiskFreeRate,RiskRate,Alpha)
{
Beta  <-  1/cumprod(1+RiskFreeRate+RiskRate)
CumulLossInflaRate <- cumprod(1+LossInflaRate)
CumulCPIRate <- cumprod(1+CPIRate)
xmin  <-  Xmin(LapseType, ElasticityLeft, WindowLeft, MinLapseRate,CentralLapseRate)
xmax  <-  Xmax(LapseType, ElasticityLeft, WindowLeft, MaxLapseRate,CentralLapseRate)
kmin  <-  OptimYear - FirstYear + 1
kmax  <-  LastYear - FirstYear + 1 
NbOfFutureSimYear <-  LastYear - OptimYear  + 1
PriorLR <- (LRUpBound*LRLowBound)^0.5
Phase  <-  1
LRScenar  <- LRScenar(PriorLR,Phase,LRUpBound ,LRLowBound,TransitCoefMean,TransitCoefStdDev,LTMeanComputYearNb)
MarketPriceLTMean  <- mean( 1/LRScenar)
MarketPriceMatrix  <- MarketPriceMatrix(NbOfFutureSimYear+1,NbOfSimScenar,Phase,LRUpBound ,LRLowBound,
        TransitCoefMean,TransitCoefStdDev,PriorMarketPricePosition,MarketPricePhase,MarketPriceLTMean)
MarketPriceMean <- apply(MarketPriceMatrix,2,mean)
Evaluate <- function(x) 
{
 ResultActuSum(FirstYear,OptimYear,x,NbOfFutureSimYear,
      CentralLapseRate, ElasticityLeft,WindowLeft, WindowRight, ElasticityRight,
      MarketPriceMean,Price,Commission,IndividualExp,IndivMeanLossAmount, 
      CPIRate,LossInflaRate,Beta,Alpha)
 }
output    <- genoud(Evaluate , nvars=NbOfFutureSimYear,Domains = cbind(xmin[kmin : kmax],xmax[kmin : kmax]), 
pop.size=5000,max=TRUE)
Solution <- rev(output$par)
MarketPriceMean <-rev(MarketPriceMean)
Solution
}    
########################################################
# Problem parameters for a given year N & the coressponding filtration in year N-1  :
########################################################
  
OptimYear <- 2017
# year of optimisation
PriorMarketPricePosition <-  1
 #market price position in OptimYear-1
MarketPricePhase <-  1 
 #market price (excluding inflation) phase in OptimYear
#1: increasing, -1: decreasing
########################################################
Xoptim <- YearFiltrationPbOptimPosition( PriorMarketPricePosition,MarketPricePhase,LRUpBound,LRLowBound,TransitCoefMean,TransitCoefStdDev,
      LTMeanComputYearNb ,
      FirstYear,LastYear,OptimYear,
      CentralLapseRate, ElasticityLeft,WindowLeft, WindowRight, ElasticityRight,
        Price,Commission,IndividualExp,IndivMeanLossAmount,
        CPIRate,LossInflaRate,RiskFreeRate,RiskRate,Alpha)

Xoptim



Best Regards 
thank you for your help


      
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