alogos.systems.whge.init_population

Initialization functions to generate a population for WHGE.

Functions

given_genotypes(grammar, parameters=None)

Create a population from given genotypes.

random_genotypes(grammar, parameters=None)

Create a population from random genotypes.

Detailed object descriptions

alogos.systems.whge.init_population.given_genotypes(grammar, parameters=None)[source]

Create a population from given genotypes.

Parameters:

  • grammar (Grammar)

  • parameters (dict or ParameterCollection) – Following keyword-value pairs are considered by this function:

    • init_pop_given_genotypes (list of Genotype objects or data that can be converted to a genotype) : A list of genotypes, which are used to initialize the individuals of the population. Note that the length of this list determines the size of the generated population.

Returns:

population (Population)

Raises:

InitializationError – If creating the population fails.

alogos.systems.whge.init_population.random_genotypes(grammar, parameters=None)[source]

Create a population from random genotypes.

Parameters:

  • grammar (Grammar)

  • parameters (dict or ParameterCollection) – Following keyword-value pairs are considered by this function:

    • population_size (int)

    • init_pop_unique_genotypes (bool)

    • init_pop_unique_phenotypes (bool)

    • init_pop_unique_max_tries (int)

Returns:

population (Population)

Raises:

InitializationError – If creating the population fails.

Notes

Choice of genotype size:

  • In the 2017 paper on HGE and WHGE the genotype size is set to 256, 512 and 1024 bits in the experiments.

  • In the 2018 paper on WHGE the genotype size is set to 1024 bits.

  • The reference implementation in Java comes with examples where the genotype size is set mostly to 256 or 1024 bits.

Construction of a population of random individuals:

  • The reference implementation in Java seems to construct random individuals independently, without checking for duplicates on genotype or phenotype level. Is is extremely unlikely to ever create two identical individuals on the genotype level (2^256 or 2^1024 possibilities), but on the phenotype level it can happen quite often (depending on structure of the grammar).

References

  • Papers

    • 2017: Medvet, Hierarchical Grammatical Evolution

      • p. 250: “For each GE variant, problem, and genotype size, we performed 30 independent executions of the evolutionary search by varying the random seed and with the following evolution parameters: population of 500 individuals randomly initialized […]”

        1. 250, Table 1: genotype sizes |g| 256, 512, 1024

    • 2018: Bartoli, Castelli, Medvet: Weighted Hierarchical Grammatical Evolution

      • p. 7: “Concerning the variant-specific parameters, we set the genotype size to 1024 bits for GE, πGE, and WHGE”

      • p. 8: “This finding could be explained by the lower degeneracy of WHGE (see Section IV-D) which results in a tendency of WHGE to better sample the phenotype space given a random set of genotypes.”

  • Reference implementation in Java: evolved-ge

    • Folder with initialization classes

    • Folder with example scripts

      • Using 256 bits: GOM.java

      • Using 1024 bits: DeepExperimenter.java, DeepDistributedExperimenter.java

      • Using 256 and 1024 bits: MapperGenerationExperimenter.java, MapperGenerationDistributedExperimenter.java

      • Using 64, 128, 256, 512 and 1024 bits: MappingPropertiesExperimenter.java