EAKit

EAKit is a Framework of Evolutionary Algorithms written in Swift. It is fully generic, architecture (algorithms) based on (interchangeable) components, simple to use and you can build your own evolutionary algorithm by using existing components.

Contents

• Features
• Requirements
• Installation
• Usage
• Components
• Custom Implementation
• Future
• License

Features

• Genetic Algorithm
• Evolutionary Strategy
• Particle Swarm
• Differential Evolution
• Hill Climbing

Requirements

• iOS 8+ / macOS 10.9+
• Xcode 10+
• Swift 5+

Installation

Carthage

github "Li-Bot/eakit.git" ~> 0.1.0

Swift Package Manager

dependencies: [
    .package(url: "https://github.com/Li-Bot/eakit.git", .upToNextMajor(from: "1.0.0"))
]

Cocoapods

pod 'EAKitAI', '~> 0.1.0'

Usage

Let's look at how simple it is!

Hill Climbing

// Parameters of Hill Climbing
let parameters = try! EAHillClimbingParameters(
    populationCount: 20,
    generationsCount: 101,
    fitnessFunction: EAAckleyFunction(),
    deviation: 0.5
)
// Run the algorithm
let algorithm = EAHillClimbing(parameters: parameters)
let result = algorithm.run()
// Show result
print(result.bestPopulation.bestIndividual?.fitness)
print(result.bestPopulation.bestIndividual?.data)

Genetic Algorithm

// Data of TSP Fitness Function
let cities = [
    EATSPCity("A", [60.0, 200.0]), EATSPCity("B", [80.0, 200.0]), EATSPCity("C", [80.0, 180.0]),
    EATSPCity("D", [140.0, 180.0]), EATSPCity("E", [20.0, 160.0]), EATSPCity("F", [100.0, 160.0]),
    EATSPCity("G", [200.0, 160.0]), EATSPCity("H", [140.0, 140.0]), EATSPCity("I", [40.0, 120.0]),
    EATSPCity("J", [100.0, 120.0]), EATSPCity("K", [180.0, 100.0]), EATSPCity("L", [60.0, 80.0]),
    EATSPCity("M", [120.0, 80.0]), EATSPCity("N", [180.0, 60.0]), EATSPCity("O", [20.0, 40.0]),
    EATSPCity("P", [100.0, 40.0]), EATSPCity("Q", [200.0, 40.0]), EATSPCity("R", [20.0, 20.0]),
    EATSPCity("S", [60.0, 20.0]), EATSPCity("T", [160.0, 20.0])
]
let fitnessFunction = EATSPFunction(cities: cities)
// Parameters of Genetic Algorithm
let parameters = try! EAGeneticAlgorithmParameters(
    populationCount: 20,
    generationsCount: 301,
    fitnessFunction: fitnessFunction,
    isElitism: true,
    selection: EARandomSelection(isElitism: true),
    crossover: EATSPTwoPointCrossover(threshold: 1.0),
    mutation: EASwapMutation(threshold: 0.9, count: 1)
)
// Run the algorithm
let algorithm = EAGeneticAlgorithm(parameters: parameters)
let result = algorithm.run()
// Show result
print(result.bestPopulation.bestIndividual?.fitness)
print(result.bestPopulation.bestIndividual?.data)

Evolutionary Strategy

// Configuration of Evolutionary Strategy 
let configuration = try! EAEvolutionaryStrategyConfiguration(µ: 20, ρ: 3, selectionStrategy: .plus, λ: 20)
// Parameters of Evolutionary Strategy
let parameters = try! EAEvolutionaryStrategyParameters(
    generationsCount: 101,
    configuration: configuration,
    fitnessFunction: EAAckleyFunction(),
    selection: EARandomSelection(),
    recombination: EAESIntermadiateRecombination(),
    mutation: EAESNormalMutation(threshold: 1.0, σ: 0.5)
)
// Run the algorithm
let algorithm = EAEvolutionaryStrategy(parameters: parameters)
let result = algorithm.run()
// Show result
print(result.bestPopulation.bestIndividual?.fitness)
print(result.bestPopulation.bestIndividual?.data)

Particle Swarm

// Parameters of Particle Swarm
let parameters = try! EAParticleSwarmParameters(
    particlesCount: 10,
    iterationsCount: 101,
    velocity: EAParticleSwarmVelocity(maximum: EASphereFunction().distance / 30.0),
    learning: .defaultLearning,
    inertiaWeight: .defaultInertiaWeight,
    fitnessFunction: EAAckleyFunction()
)
// Run the algorithm
let algorithm = EAParticleSwarm(parameters: parameters)
let result = algorithm.run()
// Show result
print(result.bestPopulation.bestIndividual?.fitness)
print(result.bestPopulation.bestIndividual?.data)

Differential Evolution

// Parameters of Differential Evolution
let parameters = try! EADifferentialEvolutionParameters(
    populationCount: 10,
    generationsCount: 101,
    selection: EARandomSelection(),
    mutationStrategy: EADERand1BinMutationStrategy(f: 0.5, λ: 0.5),
    crossover: EADifferentialEvolutionCrossover(cr: 0.9),
    fitnessFunction: EAAckleyFunction()
)
// Run the algorithm
let algorithm = EADifferentialEvolution(parameters: parameters)
let result = algorithm.run()
// Show result
print(result.bestPopulation.bestIndividual?.fitness)
print(result.bestPopulation.bestIndividual?.data)

Components

Each evolutionary algorithm is decomposed to components. The components are interchangeable between algorithms. Each component is defined by protocol.

Find out more about components here.

Custom Implementation

Let's look at how simple it is to implement your own components & evolutionary algorithms.

Find out more about custom implementation here.

Future

• More evolutionary algorithms
• More components
• More built-in functions

License

EAKit is released under the GNU GPLv3 license. See the LICENSE here.