úÎ 9|6;      !"#$%&'()*+,-./0123456789: .The fitness function for the chromosome model /The mutation operator for the chromosome model 0The crossover operator for the chromosome model $The config for the chromosome model $The config for the population model CThe function that transforms a population into the next generation $The fitness at which to stop the GA 'The generation at which to stop the GA @The number of generations elapsed. defaultConfig sets this to 0 The random number generator <defaultConfig acts as a blank slate for genetic algorithms. H cConfig, pConfig, gen, and maxFitness or maxGeneration must be defined CWrapper function which returns the best chromosome of a population ;JWrapper function which returns the highest-fitness member of a population CA wrapper function for use in newPopulation for roulette selection EA wrapper function for use in newPopulation for tournament selection HA wrapper function for use in newPopulation for mutating the population UA wrapper function for use in newPopulation for applying crossover to the population HRuns the specified GA config until the termination condition is reached [Returns true if the given population satisfies the termination condition for the GA config TGenerates a random number which updating the random number generator for the config        ZConfig for use of lists as the population model. Lists are deprecated in favor of arrays. <?The type used to represent population arrays; is a diff array. Population config for arrays  =  !UThe config for a chromosome of a list of bits. User must defined fitness and mutate. "(Single point cross at a random location #Generates i random bits $1Randomly flips fits with a specified probability %Converts a list of Bool's to it's integer representation !"#$%$%#"!!"#$% &jA node in an ANN. The head of the list is the bias weight. The tail is the weights for the previous layer 'A layer of nodes in an ANN (An Artificial Neural Network )rReturns the number of examples correct within the tolerance. The examples are a list of tuples of (input, output) *KComputes the fitness based on the mean square error for a list of examples 6 The examples are a list of tuples of (input, output) +6Computes the mean square error for a list of examples 6 The examples are a list of tuples of (input, output) ,0Mutates an ANN by randomly settings weights to +/- range -0Mutates an ANN by randomly shifting weights by +/- range .Crossover between two ANN's by exchanging weights /Crossover between two ANN's by averaging weights 0$Evaluates an ANN with a given input 1|Generates a random ANN with a given number of input nodes, a list of number of hidden nodes per layer, and the weight range &'(>)*+,-./01 ('&0./,-*+)1 &'()*+,-./012$A node in the syntax tree of the GP 4)An operator in the syntax tree of the GP 6&The function for evaluating this node 7$The number of children of this node 8 The name of the node when shown 9LCalculates fitness based on the mean square error across a list of examples E The examples are a list of tuples of (inputs state, correct output) : Statefully evaluates a given GP 2345678?9:@A 45678239: 2334567856789:B       !"###$%&'()*+,-./0123((44567829:;##<= hgalib-0.2GAPopulation.ListPopulation.ArrayChromosome.BitsChromosome.ANN Chromosome.GPPopulationConfigbestChromosomePop roulettePop tournamentPopapplyCrossoverPopapplyMutationPopChromosomeConfigfitnessmutatecrossConfigcConfigpConfig newPopulation maxFitness maxGenerationcurrentGenerationgenGAState defaultConfigbestChromosome rouletteM tournamentMmutateMcrossMrunisDonegaRandconfig pointCross randomBits mutateBitsbits2intNodeLayerANNcorrectExamples fitnessMSE averageMSEmutateRandomize mutateShift uniformCross averageCrosseval randomANNOpcallbackarityname mseFitnesshighestFitnessPArrayfromListrandom