Optimizers

Optimizers are used in conjugation with single generation worlds (or within multi-generation worlds). Optimizers take a population and use results of the evaluations performed by worlds to select parents which are used to generate a new population. Depending on the type of optimizer being used, the new generation may be all new organisms or a mix of new organisms and organisms from previous generations.

optimizeValue

each optimizer will define parameters which determine what values will be optimized. Most commonly, this parameter will be called 'optimizeValue'. Optimizers may be created that optimize on more than one term (i.e. multi-objective) and these optimizers will define parameters as needed.

Optimizers will usually use an MTree for the optimizeValue parameter. This allows the user to determine what elements from organisms (DataMaps)[DataMap] will be used and how.

Asexual vs. Sexual Reproduction

Asexual optimizers select single parent organisms and then use them to generate offspring with are collected into a new population. Sexual optimizers select two or more parents and generate offspring from these. In either case, in the code, generally, this is handled with a call to makeMutatedOffspring(), which in turn manages coping organisms, recombination, mutations, etc. based on the type of genome and brain being used.

Optimizer Types

GA

Selects a single parent organism at a time and then calls makeMutatedOffspringFrom() with this organism to produce a new organism which is added to a new population. Parents are selected with a method that makes is more likely that organisms with high score relitive to the population will reproduce, but still allows for the change that even the worst organism may reproduce.

Tournament

Selects a single parent organism at a time and then calls makeMutatedOffspringFrom() with this organism to produce a new organism which is added to a new population. To select each parent organism a number of organisms are selected at random from the population. From this selection, the organism with the highest score is used as the parent.

Tournament2

Selects a two parent organisms at a time and then calls makeMutatedOffspringFromMany() with these organisms to produce a new organism which is added to a new population. Although this is sexual reproduction, the gender of the organisms is ignored. The two parents are picked using the method defined in Tournament.

(variableMethod)

not implemented yet - selects one parent and then inspects that parents genome to determine if this parent will reproduce sexually or asexually and if sexually, which sexual trait will be used.

(multi objective)

not implemented yet

(lambda lambda)

not implemented yet