EvolutionStrategy.int: Evolution Strategy with Integer Chromosomes
In fnoorian/gramEvol: Grammatical Evolution for R

EvolutionStrategy.int

R Documentation

Evolution Strategy with Integer Chromosomes

Description

Uses evolution strategy to find the minima of a given cost function. It evolves chromosomes with limited-range integers as codons.

Usage

EvolutionStrategy.int(genomeLen, codonMin, codonMax, 
    genomeMin = rep.int(codonMin, genomeLen), 
    genomeMax = rep.int(codonMax, genomeLen), 
    suggestion = NULL, popSize=4, newPerGen = 4,
    iterations = 500, terminationCost = NA, 
    mutationChance = 1/(genomeLen+1), 
    monitorFunc = NULL, evalFunc, allowrepeat = TRUE, 
    showSettings = FALSE, verbose = FALSE, plapply = lapply)

Arguments

`genomeLen`	Number of integers (i.e, codons) in chromosome.
`codonMin`	Minimum integer value range for all codons.
`codonMax`	Maximum integer value range for all codons.
`genomeMin`	A vector of length `genomeLen` containing fine-grained control over each codon's minimum. Overrides `codonMin`.
`genomeMax`	A vector of length `genomeLen` containing fine-grained control over each codon's maximum. Overrides `codonMax`.
`suggestion`	A list of suggested chromosomes to be used in the initial population.
`popSize`	Size of the population generated by mutating the parent.
`newPerGen`	Number of the new randomly generated chromosome in each generation.
`iterations`	Number of generations to evolve the population.
`terminationCost`	Target cost. If the best chromosome's cost reaches this value, the algorithm terminates.
`mutationChance`	The chance of a codon being mutated. It must be between 0 and 1.
`monitorFunc`	A function that is called at each generation. Can be used to monitor evolution of population.
`evalFunc`	The cost function.
`allowrepeat`	Allows or forbids repeated integers in the chromosome.
`showSettings`	Enables printing GA settings.
`verbose`	Enables verbose debugging info.
`plapply`	`lapply` function used for mapping chromosomes to the cost function. See details for parallelization tips.

Details

EvolutionStrategy.int implements evolutionary strategy search algorithm with chromosomes created from integer values in the range of codonMin to codonMax. genomeMin and genomeMax allow fine-grained control of range for individual codons. It first creates an initial population, using suggested input suggestion or a randomly generated chromosome. Score of each chromosome is evaluated using the cost function costFunc. If the best chromosome reaches terminationCost, the algorithm terminates; otherwise only the best candidate is selected and mutated to create a new generation, and the cycle is repeated. This iteration continues until the required cost is reached or the number of generations exceeds iterations.

At each generation, the supplied monitorFunc is called with a list similar to EvolutionStrategy.int returning value as its argument.

The evalFunc receives integer sequences and must return a numeric value. The goal of optimization would be to find a chromosome which minimizes this value.

To parallelize cost function evaluation, set plapply to a parallelized lapply, such as mclapply from package parallel. In functions that do not handle data dependencies such as parLapply, variables and functions required for correct execution of evalFunc must be exported to worker nodes before invoking EvolutionStrategy.int.

Value

A list containing information about settings, population, and the best chromosome.

`settings$genomeMin`	Minimum of each codon.
`Settings$genomeMax`	Maximum of each codon.
`settings$popSize`	Size of the population created using mutation.
`settings$newPerGen`	Number of the new randomly generated chromosome in each generation.
`settings$totalPopulation`	Size of the total population.
`settings$iterations`	Number of maximum generations.
`settings$suggestion`	Suggested chromosomes.
`settings$mutationChance`	Mutation chance.
`population$population`	The genomic data of the current population.
`population$evaluations`	Cost of the latest generation.
`population$best`	Historical cost of the best chromosomes.
`population$mean`	Historical mean cost of population.
`population$currentIteration`	Number of generations evolved until now.
`best$genome`	The best chromosome in integer sequence format.
`best$cost`	The cost of the best chromosome.

Examples


# define the evaluate function
evalfunc <- function(l) {
    # maximize the odd indices and minimize the even indices
    # no repeated values are allowed
    odd <- seq(1, 20, 2)
    even <- seq(2, 20, 2)
    err <- sum(l[even]) - sum(l[odd]);

    stopifnot(!any(duplicated(l))) # no duplication allowed

    return (err)
}

monitorFunc <- function(result) {
    cat("Best of gen: ", min(result$best$cost), "\n")
}

x <- EvolutionStrategy.int(genomeLen = 20, codonMin = 0, codonMax = 20,
                allowrepeat = FALSE, terminationCost = -110,
                monitorFunc = monitorFunc, evalFunc = evalfunc)

print(x)

best.result <- x$best$genome
print("Odds:")
print(sort(best.result[seq(1, 20, 2)]))
print("Evens:")
print(sort(best.result[seq(2, 20, 2)]))

fnoorian/gramEvol documentation built on July 5, 2023, 6:38 p.m.