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Parallel Problem Solving from Nature, PPSN XI - 11th International Conference, Krakov, Poland, September 11-15, 2010, Proceedings, Part I (Paperback, Edition.)
Robert Schaefer, Carlos Cotta, Joanna Kolodziej, Gunter Rudolph
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R3,102
Discovery Miles 31 020
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Ships in 10 - 15 working days
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We are very pleased to present to you this LNCS volume, the
proceedings of the 11th International Conference on Parallel
Problem Solving from Nature (PPSN 2010). PPSN is one of the most
respected and highly regarded c- ference series in evolutionary
computation, and indeed in natural computation
aswell.Thisbiennialeventwas?rstheldinDortmundin1990,andtheninBr-
sels (1992), Jerusalem (1994), Berlin (1996), Amsterdam (1998),
Paris (2000), Granada (2002), Birmingham (2004), Reykjavik (2006)
and again in Dortmund in 2008. PPSN 2010 received 232 submissions.
After an extensive peer review p- cess involving more than 180
reviewers, the program committee chairs went through all the review
reports and ranked the papers according to the revi- ers'comments.
Each paper wasevaluated by at least three reviewers.Additional
reviewers from the appropriate branches of science were invoked to
review into disciplinary papers. The top 128 papers were ?nally
selected for inclusion in the proceedings and presentation at the
conference. This represents an acceptance rate of 55%, which
guarantees that PPSN will continue to be one of the c- ferences of
choice for bio-inspired computing and metaheuristics researchers
all over the world who value the quality over the size of a
conference. The papers included in the proceedingsvolumes covera
wide range of topics, fromevolutionarycomputationto
swarmintelligence, frombio-inspiredcomp- ing to real-world
applications. Machine learning and mathematical games s-
portedbyevolutionaryalgorithmsaswellasmemetic,agent-orientedsystemsare
also represented. They all are the latest and best in natural
computation. The proceedings are composed of two volumes divided
into nine thematic sections.
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Parallel Problem Solving from Nature, PPSN XI - 11th International Conference, Krakov, Poland, September 11-15, 2010, Proceedings, Part II (Paperback, Edition.)
Robert Schaefer, Carlos Cotta, Joanna Kolodziej, Gunter Rudolph
|
R1,643
Discovery Miles 16 430
|
Ships in 10 - 15 working days
|
We are very pleased to present to you this LNCS volume, the
proceedings of the 11th International Conference on Parallel
Problem Solving from Nature (PPSN 2010). PPSN is one of the most
respected and highly regarded c- ference series in evolutionary
computation, and indeed in natural computation
aswell.Thisbiennialeventwas?rstheldinDortmundin1990,andtheninBr-
sels (1992), Jerusalem (1994), Berlin (1996), Amsterdam (1998),
Paris (2000), Granada (2002), Birmingham (2004), Reykjavik (2006)
and again in Dortmund in 2008. PPSN 2010 received 232 submissions.
After an extensive peer review p- cess involving more than 180
reviewers, the program committee chairs went through all the review
reports and ranked the papers according to the revi- ers'comments.
Each paper wasevaluated by at least three reviewers.Additional
reviewers from the appropriate branches of science were invoked to
review into disciplinary papers. The top 128 papers were ?nally
selected for inclusion in the proceedings and presentation at the
conference. This represents an acceptance rate of 55%, which
guarantees that PPSN will continue to be one of the c- ferences of
choice for bio-inspired computing and metaheuristics researchers
all over the world who value the quality over the size of a
conference. The papers included in the proceedingsvolumes covera
wide range of topics, fromevolutionarycomputationto
swarmintelligence, frombio-inspiredcomp- ing to real-world
applications. Machine learning and mathematical games s-
portedbyevolutionaryalgorithmsaswellasmemetic,agent-orientedsystemsare
also represented. They all are the latest and best in natural
computation. The proceedings are composed of two volumes divided
into nine thematic sections.
Genetic algorithms today constitute a family of e?ective global
optimization methods used to solve di?cult real-life problems which
arise in science and technology. Despite their computational
complexity, they have the ability to explore huge data sets and
allow us to study exceptionally problematic cases in which the
objective functions are irregular and multimodal, and where
information about the extrema location is unobtainable in other
ways.
Theybelongtotheclassofiterativestochasticoptimizationstrategiesthat,
during each step, produce and evaluate the set of admissible points
from the search domain, called the random sample or population. As
opposed to the Monte Carlo strategies, in which the population is
sampled according to the uniform probability distribution over the
search domain, genetic algorithms modify the probability
distribution at each step. Mechanisms which adopt sampling
probability distribution are transposed from biology. They are
based mainly on genetic code mutation and crossover, as well as on
selection among living individuals. Such mechanisms have been
testedbysolvingmultimodalproblemsinnature, whichiscon?rmedinpart-
ular by the many species of animals and plants that are well ?tted
to di?erent ecological niches. They direct the search process,
making it more e?ective than a completely random one (search with a
uniform sampling distribution). Moreover,
well-tunedgenetic-basedoperationsdonotdecreasetheexploration
ability of the whole admissible set, which is vital in the global
optimization process. The features described above allow us to
regard genetic algorithms as a new class of arti?cial intelligence
methods which introduce heuristics, well tested in other ?elds, to
the classical scheme of stochastic global searc
Genetic algorithms today constitute a family of e?ective global
optimization methods used to solve di?cult real-life problems which
arise in science and technology. Despite their computational
complexity, they have the ability to explore huge data sets and
allow us to study exceptionally problematic cases in which the
objective functions are irregular and multimodal, and where
information about the extrema location is unobtainable in other
ways.
Theybelongtotheclassofiterativestochasticoptimizationstrategiesthat,
during each step, produce and evaluate the set of admissible points
from the search domain, called the random sample or population. As
opposed to the Monte Carlo strategies, in which the population is
sampled according to the uniform probability distribution over the
search domain, genetic algorithms modify the probability
distribution at each step. Mechanisms which adopt sampling
probability distribution are transposed from biology. They are
based mainly on genetic code mutation and crossover, as well as on
selection among living individuals. Such mechanisms have been
testedbysolvingmultimodalproblemsinnature,whichiscon?rmedinpart-
ular by the many species of animals and plants that are well ?tted
to di?erent ecological niches. They direct the search process,
making it more e?ective than a completely random one (search with a
uniform sampling distribution).
Moreover,well-tunedgenetic-basedoperationsdonotdecreasetheexploration
ability of the whole admissible set, which is vital in the global
optimization process. The features described above allow us to
regard genetic algorithms as a new class of arti?cial intelligence
methods which introduce heuristics, well tested in other ?elds, to
the classical scheme of stochastic global search.
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