Building Scalable Applications That Implement Genetic Algorithms with Genetic.io

@GeneticIO NoSQLMatters 2015 #NoSQLMatters

NoSQL Matters Paris – Mar 27, 2015

Genetic.io

Yves CorsaireJulien Sebrien

[email protected]

Building Scalable Applications That Implement Genetic Algorithms

for free


Genetic Algorithm?

A genetic algorithm (GA) is a search technique used to find exact or approximate solutions to optimization and search problems

Wikipedia ;)


Sure…


Show me an real life

example!


Satellite orbit selection!

No kidding ! http://goo.gl/eauC32

philae

tchoury


What do I need?

• A genetic encoding of each solutionEach individual holds information already present in the problem (e.g. velocity, weights), and information that needs to be computed or derived.

• A selection method : Determines how individuals are retained between each generation (Fitness Proportionate, Tournament, Rank).

• A fitness functionEvaluates the "goodness" of each individual (candidate solution).

.


Evolution process

Build Initial Population Evaluation

Terminated?

Mutation

Crossover

Selection

Done!

Yes

No


Problem Modeling

find a route to « tchoury » comet

a sequence of n chromosomes, each chromosome being a unitary 2D movement

the closest from the comet a candidate is, the highest is its rate.

f(x) = 1/ R (R=remaining distance from comet)

• Requirements

• Candidate Implementation

• Fitness Function


Selection

Multiple ways to select individuals exist : Fitness Proportionate, Tournament, Rank, etc.

Tournament Example

1.Randomly select two individuals from the population.

2.Generate a random value to decide whether to select the fitter individual or the weaker one.

3.Add the chosen individual to the current selection. The two individuals are then returned to the original population and can be selected again.

This type of selection tends to favor the fitter one more as the tournament size increases.


Crossover

NE N E NE S W SW E

E NE N NW W E E NW

one crossover point one crossover point

parent 1 parent 2

child 1 child 2

NE N E NE S E E NW

E NE N NW W W SW E


MutationObjective : inject diversity into the population, prompting the genetic algorithms to explore new solutions and as such lower the risk of being trapped in a local optimum

Description : generate a random number. If it’s less than the chosen probability of mutation (ie. Mutation Rate), then mutation takes place, else aborts.

Graph from : http://www.codeproject.com/Articles/707505/Genetic-Algorithms-Demystified

child 2

… W …

… E …


Evaluation

Fitness Function = remaining distance

tchoury

philaephilae

philae

philaephilae

philae

philae

philae

philae

philae

philaephilae


Terminated?

The algorithm terminates once at least one stopping criteria is met:

1. A pre-determined number of iterations or generations is reached.

2. A candidate that meets or exceeds a target fitness value is found.

3. The algorithm runs for a too much amount of time.

…


What choice do we have?


Current librairies

• Numerous exist (JGAP, ECJ, EpochX) in multiple languages (Java, C++, Python, etc.)

• None of them actually :

– Provide Modern Web UIs…

– Work with multiple languages…

– Run on distributed architectures…


Until now;)


Easy Set Up• Install Genetic.io using provided scripts (windows, unix, mac)

• Create user accounts

• Import your data using various importation methods (local file, upload, remote database, etc.)

• Configure your job parameters (evolution engine type, classes dependencies, termination conditions, etc.)

• Set up fitness computation parallelism method (spark, jppf, multitheading)

• Start and see realtime results!


Software Stack

Web Application Server

Web Client

HTTPWebSocket

Database Cluster

Grid Computing Cluster

Batch

CQL

CQL

Wire

Wire Data Computing Cluster


Infrastructure

start genetic jobs, get re

al-time progress

Store/Loadaccounts, datasources, jobs conf.

compute candidates fitnesses

Load

can

dida

tes

(with

dat

a lo

cality

)

Load jobs configuration, store progress


Data Importation


Data conversion


Fitness Calculator

public class PhilaeFitnessCalculator implements FitnessCalculator{@Overridepublic double calculate(IndividualBean bean, ...) {

double remainingDistance = computeRemainingDistance(bean);

return 1/remainingDistance;}@Overridepublic boolean isNatural() {

return true;}

private double computeRemainingDistance(IndividualBean bean){return ...;

}}


Crossover Operatorpublic class PhilaeCrossoverOperator implements CrossoverOperator {

...@Overridepublic List<IndividualBean> crossover(IndividualBean firstParent, IndividualBean

secondParent, Random rng) {IndividualBean firstChild = new IndividualBean(firstParent);IndividualBean secondChild = new IndividualBean(secondParent);int numberOfCrossoverPoints = getNbCrossoverPoints(); for (int i = 0; i < numberOfCrossoverPoints; i++){ int max = Math.min(firstChild.getNbAttributes(),

secondChild.getNbAttributes()); if (max > 1){

int crossoverIndex = (1 + rng.nextInt(max - 1)); for (int j = 0; j < crossoverIndex; j++){ Object temp = firstChild.getValue(j); firstChild.setValue(j, secondChild.getValue(j)); secondChild.setValue(j, temp); } }}return Arrays.asList(firstChild, secondChild);

}@Overridepublic int getNbCrossoverPoints() {

return this.nbCrossoverPoints;}...

}


Mutation Operator

public class PhilaeMutationOperator implements MutationOperator{...@Overridepublic IndividualBean mutate(IndividualBean individual, Random rng) {

individual.setValue("direction", mutateDirection(individual.getValue("direction"),

rng));return individual;

}private String mutateDirection(String direction, Random rng) {

return ...;}

}


Job Configuration


Fitness Score


Custom Candidate Display


Features• Embedded Secure User Account Management

• Data locality on most popular distributed databases (MongoDB, Cassandra)

• Parallelized Fitness Function Computation with Spark, JPPF or multithreading

• Fully Distributed Job Processing with JPPF

• Customisable candidates representation with D3.js SVG shapes

• Ability to replay jobs for in-depth algorithm evolution diagnosis

• Multilanguage (Java, Scala…), multiplatform (Windows, Unix, Mac)

• Open source!


Demo!


Questions?

Technology

Building Scalable Applications That Implement Genetic Algorithms with Genetic.io