goevo

README

GoEvo Evolutionary Algorithms

GoEvo is an evolutionary algorithms package for Go that provides both flexible and fast implementations of many genetic algorithms.

Some Key Features:

• Many Algorithms: Support for many types of evolutionary algorithms, from basic hill-climbers to a full NEAT implementation.
• Optimise Anything: NEAT genotypes, slices of floats, or any type that you can perform crossover, mutation, and fitness evaluation on are supported by this package.
• Flexible for Your Use-Case: As long as your components (such as mutation functions, selection functions, etc) implement the easy-to-understand interfaces specified, you can implement interesting and unique custom behaviour.

Documentation

The documentation is stored on the GoEvo Wiki.

Built-In Features List

Algorithms

• NEAT (Neuro Evolution of Augmenting Topologies)
• Simple one-species population
• One-by-one replacement

Genotypes

• NEAT
  • HyperNEAT is also supported
• Float array
• Boolean array

Documentation

Index

• Variables
• func Activate(x float64, a Activation) float64
• type Activation
  • func (a Activation) MarshalJSON() ([]byte, error)
  • func (a Activation) String() string
  • func (a *Activation) UnmarshalJSON(bs []byte) error
• type Agent
  • func NewAgent[T any](gt T) *Agent[T]
• type Counter
  • func NewCounter() *Counter
  • func (c *Counter) Next() int
• type Forwarder
• type GeneticDistance
• type Reproduction
• type Selection

Variables

var AllActivations = []Activation{Relu, Linear, Sigmoid, Tanh, Sin, Cos, Binary, Reln, Relum, Sawtooth, Abs}

AllActivations is a list of all possible activations.

Functions

func Activate

func Activate(x float64, a Activation) float64

Types

type Activation

type Activation int

Activation is an enum representing the different activation functions that can be used in a neural network.

const (
	Relu Activation = iota
	Linear
	Sigmoid
	Tanh
	Sin
	Cos
	Binary
	Relum
	Reln
	Sawtooth
	Abs
)

func (Activation) MarshalJSON

func (a Activation) MarshalJSON() ([]byte, error)

MarshalJSON implements json.Marshaler.

func (Activation) String

func (a Activation) String() string

String returns the string representation of the activation.

func (*Activation) UnmarshalJSON

func (a *Activation) UnmarshalJSON(bs []byte) error

UnmarshalJSON implements json.Unmarshaler.

type Agent

type Agent[T any] struct {
	Genotype T
	Fitness  float64
}

Agent is a container for a genotype and its fitness. The genotype can be of any type.

func NewAgent

func NewAgent[T any](gt T) *Agent[T]

NewAgent creates a new agent with the given genotype.

type Counter

type Counter struct {
	// contains filtered or unexported fields
}

Counter is a simple counter that can be used to generate unique IDs.

func NewCounter

func NewCounter() *Counter

NewCounter creates a new counter, starting at 0.

func (*Counter) Next

func (c *Counter) Next() int

Next returns the next value of the counter

TODO(make this thread-safe)

type Forwarder

type Forwarder interface {
	Forward([]float64) []float64
}

Forwarder is an interface for somthing that can take a set of inputs ([]float64) and return a set of outputs.

type GeneticDistance

type GeneticDistance[T any] interface {
	DistanceBetween(a, b T) float64
}

GeneticDistance is an interface for calculating the genetic distance between two genotypes.

type Reproduction

type Reproduction[T any] interface {
	// Reproduce creates a new genotype from the two parents, where the first parent is fitter
	Reproduce(a, b T) T
}

Reproduction is an interface for the reproduction of two parents to create a child

type Selection

type Selection[T any] interface {
	// SetAgents sets the agents to select from for this generation.
	SetAgents(agents []*Agent[T])
	// Select returns an agent selected from the population.
	Select() *Agent[T]
}

Selection is a strategy for selecting agents from a population. It acts on agents of type T.