41#      !"None%&*A function and its symbolic representationOEither a terminal symbol or a three-address code (a function and two pointers) !A chromosome is a vector of genes     None +Evaluate each subexpression in a chromosome Chromosome to evaluateVariable values(Resulting vector of multiple evaluations   None#&Randomly draw an element from a vector$Modify value with probability p%$Randomly generate Int between 0 and nj. Should be strictly less than n if n > 1 or zero otherwise. Therefore, getMaxInt 1 should be always 0.#$The probability pModification function% n& '()*+#$%&#$%&NoneA function to minimize.NThe argument is a vector evaluation function whose input is a vector (length c'vars1) and ouput is a vector with a different length c'length.DThe result is a vector of the best indices and a scalar loss value."Probability of constant generationjProbability of variable generation. The probability of operator generation is inferred automatically as 1 - p'const - p'var.Mutation probabilityCrossover probabilityThe chromosome lengthA (sub)population size7Number of subpopulations (1 or more) [not implemented]3Functions pool with their symbolic representationsThe input dimensionality defaultConfig = Config { p'const = 0.1 , p'var = 0.4 , p'mutation = 0.1 , p'crossover = 0.9 , c'length = 50 , c'popSize = 100 , c'popN = 1 , c'ops = V.empty -- <-- To be overridden , c'vars = 1 } ,=Evaluates a chromosome according to the given loss function."Randomly generate a new population?Selection operator that produces the next evaluated population.zStandard algorithm: the best offspring O replaces the worst individual W in the current population if O is better than W.Binary tournament selectionUniform crossover operator ;Mutation operator with up to three mutations per chromosome!AMutation operator with a fixed mutation probability of each gene"mRandomly initialize a new chromosome. By definition, the first gene is terminal (a constant or a variable).-Produce a new random gene.(A randomly generated variable identifier/-A random operation from the operations vector0,Draw a constant from the normal distribution1-Draw a constant from the uniform distribution ,Common configurationCustom loss functionMutation CrossoverIA chromosome selection algorithm. Does not need to be random, but may be.Evaluated populationNew generation23 Common configuration!Probability of gene mutationCommon configuration"Common configuration-!Probability to produce a constant!Probability to produce a variableNumber of input variablesOperations vectorMaximal operation index4 Probability p of a constant generation. 1-p2 will be the probability of a variable generation.Number of input variables./0Mean Std deviation1 , !"  ,23 !"-4./01None  !"   !"5   !"#$%&'()*+,-./ 0 1 2 3456789:;<=>?@!hmep-0.0.0-IqQ7HFTdMtZFDqXbT6JM0SMEP MEP.TypesMEP.Run MEP.Random MEP.Operators5mersenne-random-pure64-0.2.2.0-560i5rQoI3i9IX3BgSFLBVSystem.Random.Mersenne.Pure64 newPureMT0monad-mersenne-random-0.2-6tqyOWwAPZD5rWNfoRrnLpControl.Monad.Mersenne.Random evalRandom runRandomRandFGeneCVarOp Chromosome Population $fShowGeneevaluate Phenotype LossFunctionConfigp'constp'var p'mutation p'crossoverc'length c'popSizec'popNc'opsc'vars defaultConfig initializeevaluateGenerationevolvebinaryTournament crossover mutation3smoothMutation newChromosomedrawwithProbability getMaxInt getNormal getDoublegetWordgetIntgetBool!random-1.1-9tceXaeYIMZ4JrKq20Egog System.RandomRandom phenotypenewnewVarnewOp newCNormalnewCswap replaceAt newTerminal