Scala actors toolkit to solve problems in generic and scalable way using genetic algorithms

The fastest way to understand library is to take a look at BackpackExample

To use library developer has to implement few things:

• custom genome class - it extends trait Genome,
• Procreator.recombine - crossover operator takes two custom genotype objects as argument and return new genotype object,
• Procreator.mutate - mutation operator takes one genotype and return its mutation,
• Evaluator.eval - objective function takes genotype as an argument and return its value,
• Godfather.initialGenomes - initial population generator should return collection of genotypes to start evolution with ( the better they are the faster good results may be obtained).

User can also manipulate algorithm's parameters. And these are:

• Controller.optimalPopulationSize - the size of population that Controller will try to reach and maintain,
• Controller.maxToKillOrCreate - the maximum number of phenotypes that controller will try to kill or create to be as close to optimalPopulationSize as possible and to replace as many phenotypes as possible, (it is clearly described in Controller.calculatePopulationChange code doc),
• Procreator.mutationProbability - probability of mutation in newly created genome,
• RandomKiller.randomKillRatio - determines the ratio of the number of random deaths to deaths,
• EndOfAlgorithm.maxTimeBetweenImprovement - timeout to stop the algorithm since last best result(default 30 seconds).

Library uses a few types of actors to model genetic algorithm. These actors are:

• Phenotype, it contains genotype,
• Godfather, holds references to population of Phenotypes.
• Evaluator, it implements the objective function so it can evaluate Phenotype value,
• Controller, it tries to keep the correct population size but also to replace effectively old phenotypes with new ones,
• EndOfAlgorithm, it makes decision when to stop algorithm. The Evaluator notify it every time when it finds the new best phenotype,
• Procreator, implements mutation and crossover operators so it can create new genotype from parents,
• DeathItself, kills specified Phenotype,
• RandomKiller, kills random phenotype from time to time.

The objective function value can be minimized or maximized. This behavior is determined by two implementations from scalagen.population package:

• MaximizeValue,
• MinimizeValue. This traits can be mixed into implementations of Controller, EndOfAlgorithm and Evaluator actors.

Controller chooses some number of phenotypes to be killed. From the rest of the population it selects pairs. They will procreate and have a child.

In package scalagen.population there are two traits with some example implementations:

• PopulationKilling, represents strategy of selecting phenotypes to be killed,
• PopulationReproduction, represents strategy of selecting parents pairs.

Type sbt in project directory. Then type projects. You can see project list containing:

• *root
• scalagen-core
• scalagen-examples

Root is your current project and it is parent project for core and examples. Type compile to compile both of them.

You can switch to projects using command project {project-name}. In scalagen-core you can perform tests using test command. In scalagen-example you can type run to run examples.

If you would like to contribute to scalagen, your help is very welcome. You may

• work on some existing issue,
• make up your own improvement,
• write some example using library.

SBT

libraryDependencies += "com.github.scalagen" % "scalagen_2.11" % "0.4.0"

Gradle

compile 'com.github.scalagen:scalagen_2.11:0.4.0'

Maven

<groupId>com.github.scalagen</groupId>
<artifactId>scalagen_2.11</artifactId>
<version>0.4.0</version>

Copyright 2014 Przemek Piotrowski
Copyright 2014 Tomasz Rozbicki

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

   http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.