Community – Page 6547 – LCS & GBML Central

Many congratulations Marc!

This week started with the master thesis (PFC) presentation of one of our scholarship holder, Marc Aguilar. I could not attend his presentation but the attendants praised the talk and told me that Marc was fantastic and confident. For this reason, I decided to interview him and you will find below his impressions.

Interviewer: […]

ZigBeeCars at the “Saló de l’ensenyament”

What does the equation ZigBee + Cars = ZigBeeCars mean?
On March 19, ZigBeeCars will be presented in the Saló de l’ensenyament in view of promoting the engineering at La Salle. In a space of 35 square meters, offered by the EnginyCat program of the Generalitat de Catalunya, a spectacular racetrack will allow the […]

GPEM 10(1) hits the streets

My hardcopy arrived in my mailbox today and it looks good! If you have a subscription yours should arrive soon.

A review of procedures to evolve quantum algorithms

Abstract There exist quantum algorithms that are more efficient than their classical counterparts; such algorithms were invented by
Shor in 1994 and then Grover in 1996. A lack of invention since Grover’s algorithm has been commonly attributed to the non-intuitive
nature of quantum algorithms to the classically trained person. Thus, the idea of using computers to automatically generate
quantum algorithms based on an evolutionary model emerged. A limitation of this approach is that quantum computers do not
yet exist and quantum simulation on a classical machine has an exponential order overhead. Nevertheless, early research into
evolving quantum algorithms has shown promise. This paper provides an introduction into quantum and evolutionary algorithms
for the computer scientist not familiar with these fields. The exciting field of using evolutionary algorithms to evolve quantum
algorithms is then reviewed.

Content Type Journal Article
Category Original Paper
DOI 10.1007/s10710-009-9080-7
Authors
- Adrian Gepp, Bond University School of Information Technology Robina QLD Australia
- Phil Stocks, Bond University School of Information Technology Robina QLD Australia

Journal Genetic Programming and Evolvable Machines
Online ISSN 1573-7632
Print ISSN 1389-2576

Journal Volume Volume 10

Journal Issue Volume 10, Number 2 / June, 2009

The ideal engineer

Nowadays our society is worried about the decrease in the number of engineers and we do not understand why the profession that ten years ago was seen the job of the future, has not turned out to be so.
On the 22nd January, the program “Einstein a la platja” on BTV raised this question with […]

Evolution of Mona Lisa

Via a post by David Oranchak, I just run into a Roger Alsing exercise to evolve paintings using polygons. I found pretty surprising the quality of the evolved painting around the eyes. The video below presents the evolution of the Mona Lisa’s painting as it moves along. The original post also provides other snapshots.

Related […]

Evolution of life in 60 seconds

Via seedmagazine.com, a very nice animation of the time scale of biological evolution on Earth. I think it makes its point beautifully, with an aesthetic that echoes Powers of Ten and The Outer Limits. The shape of the underlying curve is probably worth keeping in mind for artificial evolutionary systems as well.

Automated feature selection in neuroevolution

Abstract Feature selection is a task of great importance. Many feature selection methods have been proposed, and can be divided generally
into two groups based on their dependence on the learning algorithm/classifier. Recently, a feature selection method that
selects features at the same time as it evolves neural networks that use those features as inputs called Feature Selective
NeuroEvolution of Augmenting Topologies (FS-NEAT) was proposed by Whiteson et al. In this paper, a novel feature selection
method called Feature Deselective NeuroEvolution of Augmenting Topologies (FD-NEAT) is presented. FD-NEAT begins with fully
connected inputs in its networks, and drops irrelevant or redundant inputs as evolution progresses. Herein, the performances
of FD-NEAT, FS-NEAT and traditional NEAT are compared in some mathematical problems, and in a challenging race car simulator
domain (RARS). On the whole, the results show that FD-NEAT significantly outperforms FS-NEAT in terms of network performance
and feature selection, and evolves networks that offer the best compromise between network size and performance.

Content Type Journal Article
DOI 10.1007/s12065-009-0018-z
Authors
- Maxine Tan, Vrije Universiteit Brussel, IBBT Department of Electronics and Informatics (ETRO) Brussel Belgium
- Michael Hartley, DownUnder Geosolutions 80 Churchill Avenue Subiaco WA 6008 Australia
- Michel Bister, Vrije Universiteit Brussel, IBBT Department of Electronics and Informatics (ETRO) Brussel Belgium
- Rudi Deklerck, Vrije Universiteit Brussel, IBBT Department of Electronics and Informatics (ETRO) Brussel Belgium

Journal Evolutionary Intelligence
Online ISSN 1864-5917
Print ISSN 1864-5909

Journal Volume Volume 1

Journal Issue Volume 1, Number 4

GECCO conference highly ranked

According to the rankings at this site, the Genetic and Evolutionary Computation Conference (GECCO) ranks 11th out of 701 considered conferences in “Artificial Intelligence / Machine Learning / Robotics / Human Computer Interaction.” The rankings are based on citation of papers, quality of referees’ reports, availability of resources to students by the conference, conference papers accepted/appeared in reputable journals after the conference, and indexing (details here).

CFP: Special Issue on Parallel and Distributed Evolutionary Algorithms

Genetic Programming and Evolvable Machines

Special Issue on Parallel and Distributed Evolutionary Algorithms

(Revised March 27, 2009; please note revised submission procedures.)

(Revised April 29, 2009; extended submission deadline.)

Genetic Programming, and Evolutionary Computation at
large have been extremely successful in the last decade across
a wide range of problems and applications. Current applications are
characterized by an ever growing complexity and a pronounced
distributed nature. While the use of centralized or hierarchical
architectures and algorithms has been dominant so far, they are
now becoming impractical because they have poor scalability and
fault-tolerance characteristics. Since evolutionary algorithms are
ideally suited to population partitioning and structuring, distributed
and parallel approaches appear to be a natural way to
cope with the growing computational burden associated with large
problems.

The aim of this Special Issue is to provide the reader with
contributions discussing recent advances and an indication of
future trends in the theory, development, and application of
parallel and distributed evolutionary algorithms. We encourage
submission of papers describing new concepts, models, and
strategies, along with papers describing systems and tools that
provide practical implementations. Papers describing either
hardware or software aspects of parallel and distributed
architectures are welcome. In addition, we are interested in
application papers discussing the power and applicability of these
parallel methods to real-world problems in any area of interest,
such as evolutionary design, optimization, and emerging fields
such as computational biology.

Subjects will include (but are not limited to):

– parallel and distributed evolutionary algorithms models

– theory of structured evolutionary algorithms

– performance evaluation of parallel and distributed
evolutionary algorithms

– applications of parallel and distributed evolutionary computing

– parallel and distributed implementations: software and
hardware aspects

Important dates:

* Paper submission deadline: May 15, 2009 [extended from April 30, 2009]
* Notification of acceptance: June 30, 2009
* Final manuscript: August 31, 2009

Paper Submission:

Authors are encouraged to submit high-quality, original work

that has neither appeared in, nor is under consideration by, other

journals. All submissions will be peer reviewed subject to the

standards of the journal. Manuscripts based on previously

published conference papers must be extended substantially.

Springer offers authors, editors and reviewers of Genetic

Programming and Evolvable Machines a web-enabled online

manuscript submission and review system. Our online system

offers authors the ability to track the review process of their

manuscript.

Manuscripts should be submitted to: http://GENP.edmgr.com. This

online system offers easy and straightforward log-in and submission

procedures, and supports a wide range of submission file formats.

All enquiries on this special issue by perspective authors should
be sent to the guest editors at the addresses below.

Guest editors:

Marco Tomassini
Information Systems Institute
University of Lausanne, Lausanne, Switzerland
marco.tomassini@unil.ch
Tel: +41 21 6923589

Leonardo Vanneschi
Department of Informatics, Systems and Communication (D.I.S.Co.)
Building U14, Office n. 2004
viale Sarca, 336
University of Milano-Bicocca, Milano, Italy
vanneschi@disco.unimib.it
Tel.: +39 02 64487874

Editor-in-Chief: Lee Spector, Hampshire College
Founding Editor: Wolfgang Banzhaf, Memorial University of Newfoundland
Journal Website: www.springer.com/10710