Adds the specified Genome. All Genomes must contain exactly the same number and type of chromosomes and genes and must be added in the same order for each genome. The initial population of genomes must contain multiple xy genome pairs with different surnames to prevent inbreeding, which would cause a lack of variation.

Overridable function creates the population of Genomes.

Determines whether the specified Object is equal to the current Object.

Evaluates the specified genome using a client-supplied custom fitness function.

Evolves the genomes based on the error summation caused by the genes in each chromosome. Unlike other algorithms, this version evolves based on the fitness of the entire genome. This function should be called once after each evaluation of the genomes. The stop condition may be when the error reaches a pre-defined optimum, or after a certain number of iterations without any improvement, or a combination thereof.

Allows an Object to attempt to free resources and perform other cleanup operations before the Object is reclaimed by garbage collection.

Returns the top n fittest genomes.

Creates a new GeneticAlgorithm object from a string created by ToString.

Serves as a hash function for a particular type.

Gets the Type of the current instance.

Loads the specified genetic algorithm from a file.

Creates a shallow copy of the current Object.

Ages the individual by performing mutations.

Code goes here to loop through and run the GA

Saves this genetic algorithm to a file.

Returns a String that represents this instance.