spatially structured metaheuristics: principles and practical applications

Spatially Structured Metaheuristics: Principles and Practical Applications

Juan Luis Jiménez LaredoUniversity of Luxembourg


Wikipedia A metaheuristic is a higher-level procedure or heuristic designed to find, generate, or select a lower-level procedure or heuristic that may provide a sufficiently good solution to an optimization problem, especially with incomplete or imperfect information or limited computation capacity [Bianchi et al.].


Wikipedia A metaheuristic is a higher-level procedure or heuristic designed to find, generate, or select a lower-level procedure or heuristic that may provide a sufficiently good solution to an optimization problem, especially with incomplete or imperfect information or limited computation capacity [Bianchi et al.].

By Sam Derbyshire


By Sam Derbyshire

by Johann "nojhan" Dréo, Caner Candan

By Sam Derbyshire


by Johann "nojhan" Dréo, Caner Candan

By Sam Derbyshire


Metaheuristics

Population

Evolutionaryalgorithms

By Sam Derbyshire



By Sam Derbyshire



Graph of acquaintances having a not-null probability of mating

By Sam Derbyshire



Graph of acquaintances having a not-null probability of mating

What kind of a graph definesa canonical EA??

By Sam Derbyshire


What kind of a graph definesa canonical EA??

The answer is: a complete graph

A population like that

is called panmictic


Then what happen if we

remove some edges??


We are defining a Spatially Structured

MetaheuristicAn individual can only mate

within a neighborhood.

Then what happen if we

remove some edges??


In panmictic populations the environmental selection

pressure is very high


But remember:The graph can be any!!


… and we can really relaxthe selection pressure and

favoring niching


Can we really do something with ALL this?

By Sam Derbyshire


The answer is YES, and we are gonna talk about 2 things:

• Diversity• Scalability By Sam Derbyshire


About diversity…


About diversity…

?


This is P2P…


Can P2P really scale !?


Simulator

J.L.J. Laredo, A.E. Eiben, M. van Steen, J. J. Merelo. EvAg: a scalable peer-to-peer evolutionary algorithm.

Genetic Programming and Evolvable Machines , 11(2):227-246. 2010. Simulator conducted experimentation!!!


Simulator


https://forja.rediris.es/svn/geneura/drmWrapper

http://www.hlrs.de/systems/platforms/nec-nehalem-cluster


1st large parallel P2P EA experiment188 computers x 8 cores x 2 threads = 3008 agents

Seamless scalability

1000par

seq

TT

upSpeed

Thanks for your attention