Documentation
¶
Overview ¶
Package genetics holds data holders and helper utilities used to implement genetic evolution algorithm
Index ¶
- Variables
- type ByOrganismFitness
- type EpochExecutorType
- type Gene
- type Genome
- type GenomeEncoding
- type GenomeReader
- type GenomeWriter
- type Innovation
- func NewInnovationForLink(node_in_id, node_out_id int, innovation_num int64, weight float64, ...) *Innovation
- func NewInnovationForNode(node_in_id, node_out_id int, innovation_num1, innovation_num2 int64, ...) *Innovation
- func NewInnovationForRecurrentLink(node_in_id, node_out_id int, innovation_num int64, weight float64, ...) *Innovation
- type MIMOControlGene
- type Organism
- type OrganismData
- type Organisms
- type ParallelPopulationEpochExecutor
- type Population
- type PopulationEpochExecutor
- type SequentialPopulationEpochExecutor
- type Species
Constants ¶
This section is empty.
Variables ¶
var (
ErrUnsupportedGenomeEncoding = errors.New("unsupported genome encoding")
)
Functions ¶
This section is empty.
Types ¶
type ByOrganismFitness ¶
type ByOrganismFitness []*Species
This is used for list sorting of species by maximal fitness
func (ByOrganismFitness) Len ¶
func (f ByOrganismFitness) Len() int
func (ByOrganismFitness) Less ¶
func (f ByOrganismFitness) Less(i, j int) bool
func (ByOrganismFitness) Swap ¶
func (f ByOrganismFitness) Swap(i, j int)
type EpochExecutorType ¶
type EpochExecutorType int
The epoch executor type definition
const ( // The sequential executor SequentialExecutorType EpochExecutorType = 0 // The parallel executor to perform reproduction cycle in parallel threads ParallelExecutorType = 1 )
type Gene ¶
type Gene struct {
// The link between nodes
Link *network.Link
// The current innovation number for this gene
InnovationNum int64
// Used to see how much mutation has changed the link
MutationNum float64
// If true the gene is enabled
IsEnabled bool
}
The Gene class in this system specifies a "Connection Gene." Nodes are represented using the NNode class, which serves as both a genotypic and phenotypic representation of nodes. Genetic Representation of connections uses this special class because it calls for special operations better served by a specific genetic representation. A Gene object in this system specifies a link between two nodes along with an "innovation number" which tells when in the history of a population the gene first arose. This allows the system to track innovations and use those to determine which organisms are compatible (i.e. in the same species). A mutation_num gives a rough sense of how much mutation the gene has experienced since it originally appeared (Since it was first innovated). In the current implementation the mutation number is the same as the weight.
func NewGene ¶
func NewGene(weight float64, in_node, out_node *network.NNode, recurrent bool, inov_num int64, mut_num float64) *Gene
Creates new Gene
func NewGeneCopy ¶
Construct a gene off of another gene as a duplicate
type Genome ¶
type Genome struct {
// The genome ID
Id int
// The parameters conglomerations
Traits []*neat.Trait
// List of NNodes for the Network
Nodes []*network.NNode
// List of innovation-tracking genes
Genes []*Gene
// List of MIMO control genes
ControlGenes []*MIMOControlGene
// Allows Genome to be matched with its Network
Phenotype *network.Network
}
A Genome is the primary source of genotype information used to create a phenotype. It contains 3 major constituents:
- A Vector of Traits
- A List of NNodes pointing to a Trait from (1)
- A List of Genes with Links that point to Traits from (1)
- A List of MIMO Control Genes with Links to different genome modules
(1) Reserved parameter space for future use. (2) NNode specifications. (3) Is the primary source of innovation in the evolutionary Genome. (4) Control genes allows to receive inputs from multiple independent genome modules and output processed signal to the
multitude of output locations
Each Gene in (3) has a marker telling when it arose historically. Thus, these Genes can be used to speciate the population, and the list of Genes provide an evolutionary history of innovation and link-building.
func NewModularGenome ¶
func NewModularGenome(id int, t []*neat.Trait, n []*network.NNode, g []*Gene, mimoG []*MIMOControlGene) *Genome
Constructs new modular genome
type GenomeEncoding ¶
type GenomeEncoding byte
Defines format of Genome data encoding
const ( // The plain text PlainGenomeEncoding GenomeEncoding = iota + 1 // The rich text in YAML YAMLGenomeEncoding )
type GenomeReader ¶
The interface to define genome reader
func NewGenomeReader ¶
func NewGenomeReader(r io.Reader, encoding GenomeEncoding) (GenomeReader, error)
Creates reader for Genome data with specified encoding format.
type GenomeWriter ¶
The interface to define genome writer
func NewGenomeWriter ¶
func NewGenomeWriter(w io.Writer, encoding GenomeEncoding) (GenomeWriter, error)
Creates genome writer with specified data encoding format
type Innovation ¶
type Innovation struct {
// Two nodes specify where the innovation took place
InNodeId int
OutNodeId int
// The number assigned to the innovation
InnovationNum int64
// If this is a new node innovation, then there are 2 innovations (links) added for the new node
InnovationNum2 int64
// If a link is added, this is its weight
NewWeight float64
// If a link is added, this is its connected trait index
NewTraitNum int
// If a new node was created, this is its node_id
NewNodeId int
// If a new node was created, this is the innovation number of the gene's link it is being stuck inside
OldInnovNum int64
// Flag to indicate whether its innovation for recurrent link
IsRecurrent bool
// contains filtered or unexported fields
}
This Innovation class serves as a way to record innovations specifically, so that an innovation in one genome can be compared with other innovations in the same epoch, and if they are the same innovation, they can both be assigned the same innovation number.
This class can encode innovations that represent a new link forming, or a new node being added. In each case, two nodes fully specify the innovation and where it must have occurred (between them).
func NewInnovationForLink ¶
func NewInnovationForLink(node_in_id, node_out_id int, innovation_num int64, weight float64, trait_id int) *Innovation
Constructor for new link case
func NewInnovationForNode ¶
func NewInnovationForNode(node_in_id, node_out_id int, innovation_num1, innovation_num2 int64, newnode_id int, old_innov_num int64) *Innovation
Constructor for the new node case
func NewInnovationForRecurrentLink ¶
func NewInnovationForRecurrentLink(node_in_id, node_out_id int, innovation_num int64, weight float64, trait_id int, recur bool) *Innovation
Constructor for a recur link
type MIMOControlGene ¶
type MIMOControlGene struct {
// The current innovation number for this gene
InnovationNum int64
// Used to see how much mutation has changed the link
MutationNum float64
// If true the gene is enabled
IsEnabled bool
// The control node with control/activation function
ControlNode *network.NNode
// contains filtered or unexported fields
}
The Multiple-Input Multiple-Output (MIMO) control Gene allows to create modular genomes, in which several groups of genes connected through single MIMO Gene and corresponding control function is applied to all inputs in order to produce outputs. This allows to build modular hierarchical genomes which can be considered as sum of constituent components and evolved as a whole and as a concrete parts simultaneously.
func NewMIMOGene ¶
func NewMIMOGene(control_node *network.NNode, innov_num int64, mut_num float64, enabled bool) *MIMOControlGene
Creates new MIMO gene
func NewMIMOGeneCopy ¶
func NewMIMOGeneCopy(g *MIMOControlGene, control_node *network.NNode) *MIMOControlGene
The copy constructor taking parameters from provided control gene for given control node
type Organism ¶
type Organism struct {
// A measure of fitness for the Organism
Fitness float64
// The error value indicating how far organism's performance is from ideal task goal, e.g. MSE
Error float64
// Win marker (if needed for a particular task)
IsWinner bool
// The Organism's phenotype
Phenotype *network.Network
// The Organism's genotype
Genotype *Genome
// The Species of the Organism
Species *Species
// Number of children this Organism may have
ExpectedOffspring float64
// Tells which generation this Organism is from
Generation int
// The utility data transfer object to be used by different GA implementations to hold additional data.
// Implemented as ANY to allow implementation specific objects.
Data *OrganismData
// The flag to be used as utility value
Flag int
// contains filtered or unexported fields
}
Organisms are Genotypes (Genomes) and Phenotypes (Networks) with fitness information, i.e. the genotype and phenotype together.
func NewOrganism ¶
Creates new organism with specified genome, fitness and given generation number
func (*Organism) CheckChampionChildDamaged ¶
Method to check if this algorithm is champion child and if so than if it's damaged
func (*Organism) MarshalBinary ¶
Encodes this organism for wired transmission during parallel reproduction cycle
func (*Organism) UnmarshalBinary ¶
Decodes organism received over the wire during parallel reproduction cycle
func (*Organism) UpdatePhenotype ¶
Regenerate the network based on a change in the genotype
type OrganismData ¶
type OrganismData struct {
// The implementation specific data object to be associated with organism
Value interface{}
}
The object to associate implementation specific data with particular organism for various algorithm implementations
type ParallelPopulationEpochExecutor ¶
type ParallelPopulationEpochExecutor struct {
// contains filtered or unexported fields
}
The population epoch executor with parallel reproduction cycle
func (*ParallelPopulationEpochExecutor) NextEpoch ¶
func (ex *ParallelPopulationEpochExecutor) NextEpoch(generation int, population *Population, context *neat.NeatContext) error
type Population ¶
type Population struct {
// Species in the Population. Note that the species should comprise all the genomes
Species []*Species
// The organisms in the Population
Organisms []*Organism
// The highest species number
LastSpecies int
// For holding the genetic innovations of the newest generation
Innovations []*Innovation
// An integer that when above zero tells when the first winner appeared
WinnerGen int
// The last generation played
FinalGen int
// Stagnation detector
HighestFitness float64
// The number of epochs when highest fitness was recorded for this population. If it was too long before
// than delta coding will be applied to avoid population's fitness stagnation
EpochsHighestLastChanged int
/* Fitness Statistics */
MeanFitness float64
Variance float64
StandardDev float64
// contains filtered or unexported fields
}
A Population is a group of Organisms including their species
func NewPopulation ¶
func NewPopulation(g *Genome, context *neat.NeatContext) (*Population, error)
Construct off of a single spawning Genome
func NewPopulationRandom ¶
func NewPopulationRandom(in, out, nmax int, recurrent bool, link_prob float64, context *neat.NeatContext) (*Population, error)
Special constructor to create a population of random topologies uses NewGenomeRand(new_id, in, out, n, nmax int, recurrent bool, link_prob float64) See the Genome constructor above for the argument specifications
func ReadPopulation ¶
func ReadPopulation(ir io.Reader, context *neat.NeatContext) (pop *Population, err error)
Reads population from provided reader
func (*Population) Verify ¶
func (p *Population) Verify() (bool, error)
Run verify on all Genomes in this Population (Debugging)
func (*Population) Write ¶
func (p *Population) Write(w io.Writer)
Writes given population to a writer
func (*Population) WriteBySpecies ¶
func (p *Population) WriteBySpecies(w io.Writer)
Writes given population by species
type PopulationEpochExecutor ¶
type PopulationEpochExecutor interface {
// Turnover the population to a new generation
NextEpoch(generation int, population *Population, context *neat.NeatContext) error
}
Executes epoch's turnover for population of organisms
type SequentialPopulationEpochExecutor ¶
type SequentialPopulationEpochExecutor struct {
// contains filtered or unexported fields
}
The epoch executor which will run execution sequentially in single thread for all species and organisms
func (*SequentialPopulationEpochExecutor) NextEpoch ¶
func (ex *SequentialPopulationEpochExecutor) NextEpoch(generation int, population *Population, context *neat.NeatContext) error
type Species ¶
type Species struct {
// The ID
Id int
// The age of the Species
Age int
// The maximal fitness it ever had
MaxFitnessEver float64
// How many child expected
ExpectedOffspring int
// Is it novel
IsNovel bool
// The organisms in the Species
Organisms Organisms
// If this is too long ago, the Species will goes extinct
AgeOfLastImprovement int
// Flag used for search optimization
IsChecked bool
}
A Species is a group of similar Organisms. Reproduction takes place mostly within a single species, so that compatible organisms can mate.
func NewSpeciesNovel ¶
Allows the creation of a Species that won't age (a novel one). This protects new Species from aging inside their first generation
func (Species) ComputeMaxAndAvgFitness ¶
Computes maximal and average fitness of species
func (Species) FindChampion ¶
Returns most fit organism for this species
Source Files