1. Optimizing evolutionary algorithms
at program level
JJ Merelo, A. M. Mora, Pedro Castillo,
Juan L. Jiménez Laredo, Carlos Fernandes
GeNeura team: http://geneura.wordpress.com
Departamento de Arquitectura y Tecnología de
Computadores: http://atc.ugr.es
University of Granada: http://www.ugr.es

2. Evolutionary Algorithms
 EAs are population-based algorithms that
rely on a certain number of parameters and
operators
Best parameter values and competence of
operators are well studied.
We know the limits of the EAs scalability
However…

3. Little attention is devoted to
evolutionary algorithm
implementation
Even as it allows to design better algorihtms and
obtain substantial improvement at the algorithm
and runtime level

4. Used tools
Monitors
Running time, used
memory, resource
usage
Profilers
Parts that are
responsible for
waisting resources

5. Profiler usage

6. Problem setup
Fitness = MaxOnes
Canonical
evolutionary
algorithm, élite = 2
Varied population and
chromosome size
Free software:
http://bit.ly/bOk3z3

7. Evolving an evolutionary algorithm
program
Eliminated
Added

8. Size always matters
Caché for
fitness
computations
Use of the tr
Perl-specific
function for
computing
fitness

9. If we are more, we'll take more
Using a profiler for
finding a bottleneck:
the sort function
Change by
Sort::Key
It improves worst-case
behavior.
Blue uses previous
slide function

10. Conclusions
Running time improves up to two orders of
magnitude (for some population sizes) through
changes in implementation.
It is convenient to always bear in mind usual
programming techniques and good practices.
Future work: apply this to complex EA libraries
such as Algorithm::Evolutionary, written in Perl,
and other languages (Lua)

11. Thank you
Questions?