# demo/claw.R In Rmalschains: Continuous Optimization using Memetic Algorithms with Local Search Chains (MA-LS-Chains) in R

```library(Rmalschains)

claw <- function(xx) {
x <- xx[1]
y <- (0.46 * (dnorm(x, -1, 2/3) + dnorm(x, 1, 2/3)) +
(1/300) * (dnorm(x, -0.5, 0.01) + dnorm(x, -1,
0.01) + dnorm(x, -1.5, 0.01)) + (7/300) *
(dnorm(x, 0.5, 0.07) + dnorm(x, 1, 0.07) + dnorm(x,
1.5, 0.07)))
return(y)
}

#use MA-LS-Chains
res.claw <- malschains(function(x) {-claw(x)}, lower=c(-3), upper=c(3), verbosity=0,
maxEvals=50000, control=malschains.control(popsize=50,
istep=300, ls="cmaes", optimum=-5))

#use only the CMA-ES local search
res.claw2 <- malschains(function(x) {-claw(x)}, lower=c(-3), upper=c(3), verbosity=0,
maxEvals=50000, control=malschains.control(ls="cmaes",
lsOnly=TRUE, optimum=-5))

#use only the Simplex local search
res.claw3 <- malschains(function(x) {-claw(x)}, lower=c(-3), upper=c(3), verbosity=0,
maxEvals=50000, control=malschains.control(ls="simplex",
lsOnly=TRUE, optimum=-5))

res.claw
res.claw2
res.claw3

x <- seq(-3, 3,length=1000)
claw_x <- NULL
for (i in 1:length(x)) claw_x[i] <- claw(x[i])

plot(x,claw_x, type="l")
points(res.claw\$sol, -res.claw\$fitness, col="red")
points(res.claw2\$sol, pch=3, -res.claw2\$fitness, col="blue")
points(res.claw3\$sol, pch=3, -res.claw3\$fitness, col="green")

# run the code several times to see that the local searches run fast but do not always
# end up in the global optimum
```

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Rmalschains documentation built on April 29, 2018, 9:03 a.m.