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Feb 6, 2018
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commit 19d315719f7404abc8ddc146446411447c9a86b2
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| #= | ||
| Parents <— {randomly generated population} | ||
| While not (termination criterion) | ||
| Calculate the fitness of each parent in the population | ||
| Children <- 0 | ||
| While | Children | < | Parents | | ||
| Use fitnesses to probabilistically select a pair of parents for mating | ||
| Mate the parents to create children c\ and c<i | ||
| Children <— Children U {ci,C2} | ||
| Loop | ||
| Randomly mutate some of the children | ||
| Parents <— Children | ||
| Next generation | ||
| Use continuous GA, not binary | ||
| include array of independent variables along with associated range | ||
| =# | ||
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| function initialize_population(populationSize::Int, chromosomeSize::Int, bounds::Array{Tuple{Float64, Float64},1}) | ||
| """ | ||
| Initializes the population based on the given population and chromosome size | ||
| Values will be from lb to ub (lower to upper bound) | ||
| """ | ||
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| # Check to see if we dont have enough bounds or have too many | ||
| if length(bounds) != chromosomeSize | ||
| println("Length of bounds ($(length(bounds))) not equal to specified chromosome size ($chromosomeSize)") | ||
| exit() | ||
| end | ||
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| # Init population | ||
| population = Array{Float64}(0,chromosomeSize+1) | ||
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| # Foreach member in population | ||
| for i = 1:populationSize | ||
| # Init the member | ||
| row = [] | ||
| # Create member elements based on bounds | ||
| for bound in bounds | ||
| push!(row, rand(bound[1]:0.10:bound[2])) | ||
| end | ||
| # Add another element to the end to keep score | ||
| push!(row, 1.0) | ||
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| # hcat row, append to 2D population matrix | ||
| population = cat(1, population, hcat(row...)) | ||
| end | ||
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| return population | ||
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| end | ||
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| function score_population(population::Array{Float64}, fitness_function::Function, populationSize::Int, chromosomeSize::Int) | ||
| """ | ||
| Args | ||
| population: The population to score. Must be 2D array of Float64 elements | ||
| fitness_function: Function to score the population | ||
| Must be able to take array as argument | ||
| For example f(x,y) should be f(A) where A[1]=x, A[2]=y | ||
| Returns scored population. Score is the last element of each chromosome (row) | ||
| in population | ||
| """ | ||
| #= 1-3, 4-6, 7-9, 10-12 | ||
| 1 2 3 4 | ||
| =# | ||
| population = population' | ||
| for i in 0:populationSize-1 | ||
| println(i) | ||
| member = population[(chromosomeSize+1)*i+1:(chromosomeSize+1)*(i+1)] | ||
| println(member) | ||
| population[(chromosomeSize+1)*(i+1)] = fitness_function(member) | ||
| end | ||
| println(population) | ||
| return population | ||
| end | ||
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| function GA(populationSize::Int, chromosomeSize::Int, fitness_function::Function, bounds::Array{Tuple{Float64, Float64},1}) | ||
| """ | ||
| Args | ||
| populationSize: Total number of chromosomes | ||
| chromosomeSize: How long each chromosome should be (variables in fitness function) | ||
| fitness_function: Function to determine fitness of each solution | ||
| Should take an array as an arg | ||
| Example: f(x,y,z) = x+y+z | ||
| should be f(X) = X[1]+X[2]+X[3] | ||
| in order to allow GA to take in any function with any | ||
| amount of args to the fitness function | ||
| bounds: 1D array of tuples, each tuple is the (lower, upper) bounds | ||
| for each variable. Both lower and upper need to be Float64 types | ||
| Length should match chromosomeSize | ||
| e.g: [(1.0,2.0), (3.5,4.5)] | ||
| """ | ||
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| # Set recombination and mutation rate, lower and upper bound | ||
| recombRate = 0.7 | ||
| mutateRate = 0.05 | ||
| maxIterations = 1 | ||
| # First initialize the population | ||
| population = initialize_population(populationSize, chromosomeSize, bounds) | ||
| i = 0 | ||
| while i < maxIterations | ||
| scoredPopulation = score_population(population, fitness_function, populationSize, chromosomeSize) | ||
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| i += 1 | ||
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| end | ||
| end | ||
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| function ackley(X) | ||
| return e - 20*exp(-0.2*sqrt((X[1]^2 + X[2]^2)/2) - exp((cos(2*pi*X[1]) + cos(2*pi*X[2]))/2)) | ||
| end | ||
| bounds = [(-2.0,2.0), (-2.0,2.0)] | ||
| GA(25,2,ackley,bounds) |