12/sept/2006 session #4 ppsn xi reykjavik, iceland jj merelo guervós dept. of computer architecture...
TRANSCRIPT
12/Sept/2006
Session #4PPSN XI
Reykjavik, Iceland
JJ Merelo GuervósDept. of Computer Architecture and Technology
U. of Granada Six Degrees of Separation to Darrell Whitley
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Everything is connected
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Improved Squeaky Wheel Optimisation for Driver Scheduling,
Uwe Aickelin, Edmund K. Burke, Jingpeng Li
SWO is an algorithm based on a construction-analysis-prioritization cycle.
Improved ISWO introduces
selection and mutation within
the solution.
Each component must prove its
fitness.Driver scheduling involves partitioning blocks of work (1 vehicle each) into legal shifts
set covering integer linear
programming problem.
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Search bias in ant colony optimization: on the role ofcompetition-balanced systems Blum, C.; Dorigo, M.
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An Evolutionary Approach to the Inference ofPhylogenetic Networks,
Juan Diego Trujillo and Carlos Cotta
Trying to find a phylogenetic network that models a set of sequences of molecular data using EAs
Networks include reticulation events
(horizontal transfer, recombination,
hybridization).
Heuristics based genetic operators.
Fitness function based on likelihood.
Network models close to evolutionary
model hidden in the data.
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Yao, X. Evolving artificial neural networks(1999) Proceedings of the IEEE, 87 (9), pp. 1423-1447
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Genetic Algorithm based on Independent ComponentAnalysis for Global Optimization
Gang Li, Kin Hong Lee, Kwong Sak Leung
ICA projects an n-dimensional set to a lower-dimensional space.
Since components are independent
of each other, they can be
independently maximized.
Solutions are comparable to other
algorithms, but fewer fitness
evaluations.
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X. Yao and Y. Liu. Fast evolution strategies
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When Do Heavy-Tail Distributions Help?Hansen, Gemperle, Auger, and Koumoutsakos
Cauchy distribution is an example of heavy-tail.
As opposed to gaussian.
Studies the probability of sampling
a better solution using different
Cauchy distribution.
Anisotropic Cauchy obtains
exceptionally good results on
Rastrigin function.
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Yao, X., Liu, Y. (1998)Towards designing artificial neural networks by evolution
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Neuroevolution with Analog Genetic EncodingPeter Dürr, Claudio Mattiussi, and Dario Floreano
Recent paper by Banzhaf et al. In Nature Reviews-Genetics request following more closely known biological facts in EC to convert it into computational evolution.
This paper uses an encoding for neural nets closer to
real genomes
Based on tokens that represent problem objects.
With operators to match
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Castillo-Valdivieso, P.A.et al. (2002) Statistical analysis of the parameters of a neuro-genetic algorithm
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Assortative mating drastically alters themagnitude of error thresholds
Gabriela Ochoa and Klaus Jaffe
Beyond the error threshold, evolved structures cannot be reproduced in the quasispecies evolution model (Eigen and Schuster).
In EC, related to the
exploration/exploitation balance.
Assortative mating produces the
highest error threshold, whereas
asexual reproduction produces the
lowest.
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Eiben, Hinterding, Michalewicz (1999)Parameter control in evolutionary algorithms
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Cumulative Step Length Adaptation on RidgeFunctions
Dirk V. Arnold
Ridge functions used to test ES.
This paper studies the
performance of
multirecombinative ES with
cumulative step length adaptation
for different ridge topologies.
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M. Herdy. Reproductive isolation as strategy parameter in
hierarchically organized evolutionstrategies
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Self-Adaptation on the Ridge Function Class: FirstResults for the Sharp Ridge
Hans-Georg Beyer and Silja Meyer-Nieberg
Different self-adaptation mechanism.
Different ridge: sharp ridge, in this
case.
Different ES: non-recombinative
strategies.
Why could it fail+
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Arabas, J., Michalewicz, Z., Mulawka, J. (1994)GAVaPS, A Genetic Algorithm with Varying
Population Size.
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Self-regulated Population Size inEvolutionary Algorithms
Carlos Fernandes and Agostinho Rosa
There are many self-regulated population algorithms: GAVaPS, APGA, ProFiGa:
Funky acronym not a requisite.
Some based on age.
SRP-EA combines CHC and GAVaPS.
Achieves better success rates, with a
penalty in the number of evaluations.
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Is Self-Adaptation of Selection Pressure andPopulation Size Possible? – a Case Study
A.E. Eiben M.C. Schut A.R. de Wilde
Self-adapting selection operators and population size can yield as good or better results than self-adapting operators.
Selection parameters are
encoded in individuals, and a
consensus value is reached.
Self-adapting selection increases
speed.
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Zitzler, E., Laumanns, M., and Thiele, L. (2001). SPEA2: Improving the strengthpareto evolutionary algorithm.
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Solving Multi-Objective Optimisation Problems Using the Potential Pareto Regions EvolutionaryAlgorithm
Nasreddine Hallam, Graham Kendall and Peter Blanchfield
Introduces Potential Pareto Regions Evolutionary Algorithm.
The fitness of an individual is equal to
the least improvement needed by that
individual in order to reach a non-
dominated status.
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K. Deb, L. Thiele, M. Laumanns, and E. Zitzler (2002). Scalable multi-objective optimization test
problems.
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Pareto Set and EMOA Behavior for SimpleMultimodal Multiobjective Functions
Mike Preuss, Boris Naujoks, and Günter Rudolph
Studies the often-disregarded Pareto set.
Changes induced in Pareto set alter the
ability of algorithms to track Pareto Front.
A measure of the quality of solution in the
solution space is needed.
Similar to S-metric in objective space.
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About Selecting the Personal Bestin Multi-objective Particle Swarm Optimization
Jürgen Branke and Sanaz Mostaghim
Selecting a good guide bodes well for the future of a particle.
But they can also memorize all non-
dominated personal best solutions.
Keeping a personal archive yields
better results than traditional
methods.
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A Particle Swarm Optimizer forConstrained Numerical Optimization
Cagnina, Esquivel, Coello
Uses a single method to handle all constraints
Results reported using standard test
functions.
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Modelling Group-ForagingBehaviour with Particle Swarms
Cecilia Di Chio, Riccardo Poli, and Paolo Di Chio
Uses a nature-inspired technique to model a naturla problem: group foraging.
Results encouraging.
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An evolutive approach for the delineation of locallabour markets
Florez, Casado, Martinez-Bernabeu
Using a GA for delineating local labour markets in Valencia, Spain.
Uses a bunch of operators:
mutation,crossover.
Better results than classical algorithms
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That's all
Thank you and enjoy the session