Www.kovan.ceng.metu.edu.tr Parallelized Evolution System Onur Soysal, Erkin Bahçeci Erol Şahin...

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Parallelized Evolution System

Onur Soysal, Erkin Bahçeci

Erol Şahin

Dept. of Computer Engineering

Middle East Technical University

http://www.kovan.ceng.metu.edu.tr

ISCIS’03

04/11/2003

ISCIS’03 www.kovan.ceng.metu.edu.tr

Evolutionary MethodsSimulation of Natural Evolution

Using evolution mechanisms to solve optimization problems

Survival of the fittest...........

Generation n

Chrom.1: 0101011001...

Chrom.2: 1100110111...

.........

Generation n+1

SelectReproduceMutate

...........

Chrom.1: 1010001110...

Chrom.2: 0011110101...

.........

Population n

Population n+1

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Genetic AlgorithmsEncoding

Binary representation

Stochastic searchGuided by measure of fitness

Problem-specific fitness function

Genetic operatorsMutation

Crossover

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Evolutionary Robotics

MotivationController: Sensor-actuator relationshipDifficult to design manually in nontrivial tasksTo be in the shoes of a robot?

Solutions of evolutionary roboticsEvolving controllers

Sensors ActuatorsController

???

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Genotype Phenotype:Generic Controller

010101 100111...

Sensor data

Actuator outputs

Convert to controller

parameters Use the controller

in robots

ControllerController

Chromosome

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Genotype Phenotype:Neural Network Controller

010101 100111...

Sensor data

Actuator outputs

Convert to neural

network weightsUse the neural network

to control robots

Chromosome

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Fitness Evaluation

Use controlleron physical robots

on simulated robots

Observe behavior

Calculate fitness value

Repeat for each generation

• for each chromosome

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Physics Based SimulationImportance of realistic simulationsPros

Much faster runtimecompared to physical implementationBehavioral similarity to physical robotsRealistic interactions

ConsHigh processing demand!

Example libraries: ODE, Vortex, Havok

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Single Machine Limitations

Computation requiredSolving Ordinary Differential Equations

Increasing complexity with more collisions

Time estimates for single computerOrder of minutes for a single evaluation

For 100 chromosomes and 100 generations• Total time > a week on a single machine

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Cluster Computing

Cheap alternative to supercomputers

Large-grained problems

Example applicationSETI@home: Search for Extraterrestrial Intelligence at home

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Cluster Computing Solution to Evolutionary Robotics

Fitness evaluations dominate computation time

Fitness evaluations are independent

Master-Slave modelMaster: GA

Slaves: Fitness evaluations

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Parallel Evolution System (PES)Parallel Genetic Algorithm libraryDistributed fitness evaluationCentral genetic algorithmSupports interoperability of multiple platforms

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PES Architecture

Server: GA

Clients: Fitness evaluation

PES-C

PES-C

PES-C

Client Application

Client Application

Client ApplicationPES-SServer

Application

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PES Communication Model

PES Network Adapter

PES-C

PES Network Adapter

PES-S

Ping mechanism

Task and result packets

ping

ping reply

task

result

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PES-S Architecture

Server Application

GA Engine

Task Manager

PES-S

Task generator

Best solutions

Network Adapter

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PES-C Architecture

Client ApplicationNetwork Adapter

Task

Fitness

PES-C

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PES Usage: Server

Server Code

#include <pes.h>int main(){

PES_INIT();for each generation{

PES_EVAL_INDIVIDUALS();PES_NEXT_GENERATION();

}}

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PES Usage: Client

Client Code

#include <pes.h>int main(){

PES_INIT();while true{

RECEIVE_TASK();EVAL_TASK();SEND_RESULT();

}}

ISCIS’03 www.kovan.ceng.metu.edu.tr

PES FeaturesMulti-platform support

Linux and Windows via PVM

Improved fault tolerancePing mechanism to detect non-responding clients

Supplies GA facilitiesBuilt-in crossover, mutation and selection functions

Simplified usageAutomatic task distribution / fitness collectionSingle line for evaluating fitness of whole populationBasic load balancing

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Our ApplicationTask: Aggregation

Getting 10 robots as close as possible

Minimal sensors and communication

Modified single layer NN for controlNo memory/learning

No world knowledge

Probabilistic inputs

Fitness evaluation 2000 steps of simulation

Total distance as fitness measure

1

2

3

1

3 2

31 2

31 2

R

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Sample Application Results

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Processor Load BalancingDynamic simulationVarying number of collisionsVarying task complexityVarying processor load

Diamonds: tasks Solid lines: Start of new generation

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Fault ToleranceProcessor 2 fails

Detected at ping at 15th sec

Task restart at 19th sec

Red lines: PingBlue lines: GenerationNumbers: Task index

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Efficiency & Speedup

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Discussions & Future Work

Generation gapAsynchronous reproduction

Dynamic host list

Progress monitor

Extension to the Internet

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PES Sources & Documentation

Source distribution under Gnu Public License (GPL)

User manual

Functional reference

Sample applications

http://www.kovan.ceng.metu.edu.tr/software/PES