Reminder: paper submission deadline for ICAISC 2013

A reminder that the deadline for submitting papers to the International Conference on Artificial Intelligence and Soft Computing (ICAISC) 2013 is November 20, 2012. This conference will be held in Zakopane, Poland, June 9-13, 2013.

On Being a Post-doc

After completing a PhD, most people who wish to stay in academia end up doing one or more post-doctoral positions. Experience as a post-doc is a prerequisite for a career in scientific research, as it is during your post-doc career that you get exposure to ideas and techniques outside of your PhD, and work with a wider range of people than you did during your PhD. The chances of going into a permanent academic position, without doing at least one post-doc, are very slim (most people who manage to do this tend to wind up in the same department they did their PhD in). I've done three post-docs, at Lincoln University in New Zealand, at the University of Sydney, and the University of Adelaide, both of which are in Australia.

So, what's it like being a post-doctoral fellow?

Basically, it sucks. Most post-docs are for two or three years. This term is fixed as the position is usually tied to a particular research grant, which is itself of fixed duration. This means that even if you do extremely well in your research, there is no guarantee of further employment after the contract ends. This means that as a post-doc, you will probably be changing jobs and cities every two years. If you're young and single, that's not entirely a bad thing: travelling and living in different places broadens your mind, can build a wide network of friendships and helps you appreciate different ways of life. Things get harder if you are a couple, as your partner also needs to find work in your new home. If you have even one child, it's a nightmare: you need to find a new school, your child faces the awful wrench of leaving their friends behind, if they're in after-school activities they need to be organised all over again, and if they have even minor health issues, finding adequate care for them can be very challenging. The stress that this can place on your relationship is enormous. In short, being a post-doc is a young (single) person's game.

If your post-doc is tied to a grant, then you will be working on someone else's project. In other words, you'll be working on something that is interesting to someone else (the grant holder). This also means that the outputs you produce (that is, papers) will be of benefit primarily to the grant holder rather than you.

While you should concentrate on doing the work you are paid to do, if you want to move up the academic ladder, then you also need to demonstrate the ability to do independent research. So, in addition to working a full-time job, you're also working part-time on your own research programme.

On top of the above are the dangers of any workplace: while most post-doc supervisors are good and kind people, they get their positions by being good researchers (or occasionally good politicians), not good managers. In the worst case, you might end up working for a narcissistic sociopath. Doing a post-doc with the wrong supervisor (or supervisors) can make your life a living hell. Sociopaths can be pretty hard to spot, too.

My experience is that it can take six months or more to find a new position, which means that shortly after starting a post-doc, you need to start looking for another. If your career is a chess game, then you need to start getting your pieces into place sooner rather than later.

To sum up, being a post-doctoral fellow means a semi-itinerant life of uncertainty and upheaval, serving the research needs of others, while also planning a future career that might not happen.

Was it all worth it for me? While there are many things I would do differently if I had the chance to do it all again, I don't want to live my life in regret: the things in my life, the good and the bad, the joy and the hurt, have all made me the person I am. But I do regret the hurt it has caused my family. Being a post-doc is hard on everyone if you have a family. It's not all bad news, though, and in a future post I'll be discussing ways in which you can make your post-doc career successful.

IEEE Transactions on Neural Networks and Learning Systems: Volume 23, Issue 10, October 2012

1. Title: Silicon-Based Dynamic Synapse With Depressing Response
Authors: Thomas Dowrick; Steve Hall; Liam J. McDaid
Page(s): 1513 - 1525
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6287129


2. Title: Self-Organizing Spiking Neural Model for Learning Fault-Tolerant Spatio-Motor Transformations
Authors: Narayan Srinivasa; Youngkwan Cho
Page(s): 1526 - 1538
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6248739


3. Title: Learning From ISS-Modular Adaptive NN Control of Nonlinear Strict-Feedback Systems
Authors: Cong Wang; Min Wang; Tengfei Liu; David. J. Hill
Page(s): 1539 - 1550
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6248726


4. Title: Synchronization Error Estimation and Controller Design for Delayed Lur'e Systems With Parameter Mismatches
Authors: Wangli He; Feng Qian; Qing-Long Han; Jinde Cao
Page(s): 1551 - 1563
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6248725&tag=1


5. Title: Reproducing Kernel Hilbert Spaces With Odd Kernels in Price Prediction
Authors: Miloš Krejník; Anton Tyutin
Page(s): 1564 - 1573
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6253266


6. Title: Neural Modeling of Episodic Memory: Encoding, Retrieval, and Forgetting
Authors: Wenwen Wang; Budhitama Subagdja; Ah-Hwee Tan; Janusz A. Starzyk
Page(s): 1574 - 1586
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6261552


7. Title: A Discrimination Analysis for Unsupervised Feature Selection via Optic Diffraction Principle
Authors: Praisan Padungweang; Chidchanok Lursinsap; Khamron Sunat
Page(s): 1587 - 1600
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6263306


8. Title: Nonnegative Blind Source Separation by Sparse Component Analysis Based on Determinant Measure
Authors: Zuyuan Yang; Yong Xiang; Shengli Xie; Shuxue Ding; Yue Rong
Page(s): 1601 - 1610
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6263307


9. Title: Multiclass Feature Selection With Kernel Gram-Matrix-Based Criteria
Authors: Mathieu Ramona; Gaël Richard; Bertrand David
Page(s): 1611 - 1623
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6264104


10. Title: Efficient Online Subspace Learning With an Indefinite Kernel for Visual Tracking and Recognition
Authors: Stephan Liwicki; Stefanos Zafeiriou; Georgios Tzimiropoulos; Maja Pantic
Page(s): 1624 - 1636
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6269106


11. Title: Feedback Control by Online Learning an Inverse Model
Authors: Tim Waegeman; Francis wyffels; Benjamin Schrauwen
Page(s): 1637 - 1648
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6269107


12. Title: Symbolic Representation of Recurrent Neural Network Dynamics
Authors: Thuan Q. Huynh; James A. Reggia
Page(s): 1649 - 1658
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6269105


13. Title: Inphase and Antiphase Synchronization in a Delay-Coupled System With Applications to a Delay-Coupled FitzHugh–Nagumo System
Authors: Yongli Song; Jian Xu
Page(s): 1659 - 1670
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6269931


14. Title: Simple and Fast Calculation of the Second-Order Gradients for Globalized Dual Heuristic Dynamic Programming in Neural Networks
Authors: Michael Fairbank; Eduardo Alonso; Danil Prokhorov
Page(s): 1671 - 1676
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6239600

Reminder: paper submission deadline for IEEE-SSCI 2013

A reminder that the deadline for the IEEE Symposium Series in Computational Intelligence 2013 is 10 October 2012. This series of symposia will be held in Singapore 16-19 April 2013.

Reminder: paper submission deadline for ICANNGA 2013

A reminder that the paper submission deadline for the International Conference on Adaptive and Natural Computing Algorithms (ICANNGA) 2013 is 8 October, 2013. This conference will be held in Lausanne, Switzerland, April 4-6, 2013.

Deadline Extension - IEEE CIS Facebook Photo Contest

This is cross-posted from the IEEE Computational Intelligence Society blog.

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Due to many requests, we have decided to extend the deadline of the Facebook Photo Contest to September 30, 2012.

There will be three winners, each of them will get a free CIS membership for 2013. The best out of the three winners will get a FREE IPAD2. More information can be found at http://www.tinyurl.com/cisfb2012. If you have any questions, please send a email to cis.socialmedia@gmail.com.  

Guest post: Write Right First Time with Brown's Eight Questions

This is a guest post by Stephen G. Matthews. Stephen is a PhD student in the Centre for Computational Intelligence at De Montfort University, UK.

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Write Right First Time with Brown's Eight Questions

I will share a method that I have found to be really useful. It's short, simple and incredibly effective: Brown's Eight Questions.

Robert Brown introduced Brown's Eight Questions (Brown, 1994/95) as part of an action learning set for improving writing. An action learning set is a group of people (ideally 5) who meet up to discuss common problems and solutions. Brown suggests applying this to writing for publication. An action learning set meets up and each member reviews each other's manuscripts face to face. I will focus on Brown's Eight Questions, but an action learning set for writing is well worth reading about in Brown's article.

So what is Brown's Eight Questions? Well, it is a set of eight questions designed to make an author think about writing before actually writing a first draft. Brown's idea, which was motivated by his experiences as a writer, reviewer and editor, comes from his observation that writers often focus on correcting a manuscript once it is written, rather than planning the manuscript before writing.

Brown’s Eight Questions

1. Who are the intended readers? - list 3 to 5 of them by name;
2. What did you do? (limit - 50 words)
3. Why did you do it? (limit - 50 words)
4. What happened? (limit - 50 words)
5. What do the results mean in theory? (limit - 50 words)
6. What do the results mean in practice? (limit - 50 words)
7. What is the key benefit for your readers? (limit - 25 words)
8. What remains unresolved? (no word limit)

As you can see these are simple questions and the word limit ensures answers are succinct. Brown's Eight Questions can help authors to gain clarity about their manuscript and also support the reader's understanding.

Brown's Eight Questions helps me to structure my thoughts, arguments and the message of a manuscript. It really is a useful method that can be applied to any form of writing such as journal articles, theses and reports. If you have not used it then give it a go!

Brown, Robert (1994/95) “Write Right First Time”, Literati Newsline Special Issue: 1-8. (Available from http://web.archive.org/web/19971014014626/http://www.mcb.co.uk/literati/write.htm)

Evolving Systems Volume 3 Issue 3

1. A dynamic split-and-merge approach for evolving cluster models
Edwin Lughofer
http://springer.r.delivery.net/r/r?2.1.Ee.2Tp.1jfPPN.C3agc4..H.WsNM.3vVy.bW89MQ%5f%5fDGVOFSD0

2. Online variational learning of finite Dirichlet mixture models
Wentao Fan and Nizar Bouguila
http://springer.r.delivery.net/r/r?2.1.Ee.2Tp.1jfPPN.C3agc4..H.WsNS.3vVy.bW89MQ%5f%5fDHJKFSJ0

3. Adaptive complex event processing for harmful situation detection
Jean-René Coffi, Christophe Marsala and Nicolas Museux
http://springer.r.delivery.net/r/r?2.1.Ee.2Tp.1jfPPN.C3agc4..H.WsNY.3vVy.bW89MQ%5f%5fDHdGFSP0

4. Sliding mode incremental learning algorithm for interval type-2 Takagi–Sugeno–Kang fuzzy neural networks
Sevil Ahmed, Nikola Shakev, Andon Topalov, Kostadin Shiev and Okyay Kaynak
http://springer.r.delivery.net/r/r?2.1.Ee.2Tp.1jfPPN.C3agc4..H.WsNe.3vVy.bW89MQ%5f%5fDJEeFSb0

5. Negotiating in dynamic environments: time-efficient automated negotiations by means of combinatorial auctions
Fabian Lang and Andreas Fink
http://springer.r.delivery.net/r/r?2.1.Ee.2Tp.1jfPPN.C3agc4..H.WsNk.3vVy.bW89MQ%5f%5fDJYaFTB0

Posting Hiatus

Next week my family and I will be moving away from Adelaide and leaving Australia permanently. We are moving so that I can take up a new, permanent, academic position. Since this move will involve a fair amount of chaos and interrupted Internet access (at least until we find a house and get the Internet connected again), posting to this blog will be quite sporadic.

IEEE Transactions on Neural Networks and Learning Systems: Volume 23, Issue 9, September 2012

1. Adaptive Pinning Control of Deteriorated Nonlinear Coupling Networks With Circuit Realization
Xiao-Zheng Jin; Guang-Hong Yang; Wei-Wei Che
Page(s): 1345 - 1355
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6222060

2. Approximate Solutions to Ordinary Differential Equations Using Least Squares Support Vector Machines
Siamak Mehrkanoon; Tillmann Falck; Johan A. K. Suykens
Page(s): 1356 - 1367
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6224185

3. Exponential Synchronization of Neural Networks With Discrete and Distributed Delays Under Time-Varying Sampling
Zheng-Guang Wu; Peng Shi; Hongye Su; Jian Chu
Page(s): 1368 - 1376
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6227362

4. Convergence and Rate Analysis of Neural Networks for Sparse Approximation
Aurèle Balavoine; Justin Romberg; Christopher J. Rozell
Page(s): 1377 - 1389
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6227360

5. In-Sample and Out-of-Sample Model Selection and Error Estimation for Support Vector Machines
Davide Anguita; Alessandro Ghio; Luca Oneto; Sandro Ridella
Page(s): 1390 - 1406
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6228541

6. Robust Exponential Stability of Uncertain Stochastic Neural Networks With Distributed Delays and Reaction-Diffusions
Jianping Zhou; Shengyuan Xu; Baoyong Zhang; Yun Zou; Hao Shen
Page(s): 1407 - 1416
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6228542

7. Online Kernel-Based Learning for Task-Space Tracking Robot Control
Duy Nguyen-Tuong; Jan Peters
Page(s): 1417 - 1425
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6230657

8. Memristor Bridge Synapse-Based Neural Network and Its Learning
Shyam Prasad Adhikari; Changju Yang; Hyongsuk Kim; Leon O. Chua
Page(s): 1426 - 1435
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6232461

9. Efficient Sparse Modeling With Automatic Feature Grouping
Leon Wenliang Zhong; James T. Kwok
Page(s): 1436 - 1447
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6238378

10. Hierarchical Approach for Multiscale Support Vector Regression
Francesco Bellocchio; Stefano Ferrari; Vincenzo Piuri; Nunzio Alberto Borghese
Page(s): 1448 - 1460
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6238377

11. Discretized-Vapnik-Chervonenkis Dimension for Analyzing Complexity of Real Function Classes
Chao Zhang; Wei Bian; Dacheng Tao; Weisi Lin
Page(s): 1461 - 1472
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6239601

12. Limit Set Dichotomy and Multistability for a Class of Cooperative Neural Networks With Delays
Mauro Di Marco; Mauro Forti; Massimo Grazzini; Luca Pancioni
Page(s): 1473 - 1485
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6241435

13. Adaptive Visual and Auditory Map Alignment in Barn Owl Superior Colliculus and Its Neuromorphic Implementation
Juan Huo; Alan Murray; Dongqing Wei
Page(s): 1486 - 1497
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6255791

14. Bidirectional Extreme Learning Machine for Regression Problem and Its Learning Effectiveness
Yimin Yang; Yaonan Wang; Xiaofang Yuan
Page(s): 1498 - 1505
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6222007

15. Enhancing Weak Signal Transmission Through a Feedforward Network
Xiaoming Liang; Liang Zhao; Zonghua Liu
Page(s): 1506 - 1512
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6227359

ECoS toolbox API?

Is anyone interested in an ECoS DLL / API? I'm thinking of wrapping the functionality in the command-line ECoS Toolbox up in a DLL and providing an API so that people could include EFuNN and SECoS in their own programs. Is this a worthwhile use of my time? Would anyone use it?

Reminder: paper submission deadline for CEC 2013

A reminder that the deadline for submitting papers to the IEEE Congress on Evolutionary Computation (CEC) 2013 is February 15, 2013. This conference will be held in Cancun, Mexico, June 20-23, 2013.

Identifying bats with ANN

An interesting paper has just come out in the Journal of Applied Ecology, which describes how ANN were used to identify thirty-four European species of bats based on their echolocation calls. This is a challenging problem, because the calls within any bat species can vary quite a lot, depending on what the bat is doing. For example, the calls that a bat uses while hunting are different to the calls that a bat uses while commuting to a hunting ground. The work described in this paper has several good features.

Firstly, they used a hierarchy of MLP ensembles to identify the species. First a level of MLP identified the geographic region (out of six) that the bat came from. Then a second level was used to identify the genus (out of seven) of the bat. Finally, an ensemble of species-specific MLP identified the species itself.

Secondly, they used a large data set to train the MLP, and performed a thorough data analysis to identify the significant features. Rather than just cramming every acoustic feature through the MLP and hoping for the best, they only used the most significant twenty-four.

Finally, they incorporated the classifiers into software called iBatsID that is freely available for anyone to use.

The authors reported a range of classification accuracies across the species, from a high of 100% to a low of 56.5%. They say that "This is almost certainly the results of our eANN [ensemble ANN] dealing with many more species". I think they're wrong when they say that, because the point of using ensembles is that the individual members of the ensemble can be highly specialised for a particular class. I suspect that the problem may be that the features they selected were not as useful for classifying the poorly-recognised species: rather than using the same twenty-four parameters for all thirty-four species, they might have gotten better results by selecting acoustic parameters for each species. Also, from the diagram (Figure 3 in the paper) it looks like they used the outputs of the regional and genus networks only to decide which groups of species MLP to use. An alternative would have been to use the output of the regional and genus MLP as input features for the following levels (similar to my approach in this paper), which would have added some more information into the classification process and probably boosted accuracy.

A final problem with this paper is that they have excluded a lot of the technical details about constructing and training the ANN, and about exactly how the different levels in the hierarchy interacted. This is probably because it is an ecology paper, not an ANN paper.

Overall, it's an interesting application, and I'm looking forward to seeing more work done on this problem in the future.

The problem with academic journals 6

In my previous posts on academic journals (see here, here, here, here, here, and here) I've discussed the major problem with academic journals in the context of the huge cost of accessing the content that the journals receive for free, as well as the importance of open-access journals. This post is concerned with another problem that is becoming apparent with journals: the declining acceptance rate for papers submitted to journals, in attempts to foster an image of exclusivity and quality.

A recent editorial by David Wardle describes a quantitative analysis he performed that compared the acceptance rates of four top-ranked ecological journals with the large open-access journal PLoS One, along with the citation rate of papers published in each. What he found was that the four traditional journals accepted less than 20% of the paper submitted to them, while PLoS One accepted around 69%. However, papers that are published in PLoS One are cited more than papers published in one of the traditional journals. His argument was that the traditional journals rejected papers that were of good scientific quality (that is, they described good work) but were not "worthy" of publication in such "august" journals, with the editors using the excuse that limited page space meant that there wasn't room to print the papers, even though they were quite good. He then goes on to explain that this exclusivity was motivated by a desire to increase the perception of quality of the journals. That is, the editors are trying to foster the impression that the journals must be really good, because they're really picky about which papers they publish.

But, the ultimate measure of the quality of a paper is how often it is cited, as that reflects how useful it is to other scientists, and papers published in the less-exclusive open-access journals are cited more. Thus, the concept that journals with low acceptance rates publish better papers is fatally flawed: these journals are rejecting papers that are scientifically sound and are useful to other scientists.

This leads me to think that the only reason the top journals are the top journals are because people think they are. If someone wants an authoritative citation to back up a statement they make in a paper, they will cite a paper in Nature or Science if they can, because these are the top journals (this doesn't happen much in computational intelligence, because very few papers in this field are published in Nature or Science). But the conclusion of Wardle's study is that acceptance rate is not a reliable metric of the quality of a journal. If anything, it is a measure of the snobbery of a journal.

The purpose of peer review (and of reviewers) is as a crap-filter for papers, to keep work that is incorrectly done or poorly presented from entering the literature. But with exclusive journals, the peer reviewers seem to be spending more time deciding which papers are significant enough to be published in the journal, rather than trying to identify flaws in the work. The whole thing reminds me of the reason the great physicist Richard Feynman quit the US National Academy of Science: because they spent most of their time deciding who was "worthy" of joining the Academy.

Not so long ago, we had to consider the quality of journals because it wasn't feasible to track the impact of a single paper. Now, with tools like Google Scholar, we can track the citation histories of individual papers. In short, the journal in which a paper is published is no longer that important: the usefulness, the contribution of the paper is what is important. By the same token, the quality of an academic is not measured by which institution they work for, but by their contributions. Unfortunately, the bean-counters who make the hiring and promotion decisions, and who make decisions on who gets competitive research funding, haven't grasped this concept yet.

Exclusive journals do not make a good contribution to science, as they keep too much useful material out of the public eye for too long: peer-reviewed open-access journals, with their more liberal acceptance rates, are more important then ever in this situation.

Final reminder: IEEE CIS Facebook Photo Competition

The IEEE Computational Intelligence Society are running a photo competition on Facebook. Go to http://www.watts.net.nz/CIS/contests/photo/2012/ or see the flyer below for further details.

The deadline is three weeks away!

Reminder: paper submission deadline for IJCNN 2013

A reminder that the deadline for submitting papers to the IEEE International Joint Conference on Neural Networks (IJCNN) 2013 is February 1, 2013. This conference will be held in Dallas, Texas, August 4-9, 2013.

Reminder: paper submission deadline for EvoStar 2013

A reminder that the paper submission deadline for EvoStar 2013 is 1 November, 2012. This conference will be held in Vienna, Austria, 3-5 April, 2013.

IEEE Transactions on Autonomous Mental Development: Volume 4, Issue 2, 2012

1. The “Interaction Engine”: A Common Pragmatic Competence Across Linguistic and Nonlinguistic Interactions
Pezzulo, G.
Page(s): 105 - 123
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6006515

2. Interactive Learning in Continuous Multimodal Space: A Bayesian Approach to Action-Based Soft Partitioning and Learning
Firouzi, H.; Ahmadabadi, M.N.; Araabi, B.N.; Amizadeh, S.; Mirian, M.S.; Siegwart, R.
Page(s): 124 - 138
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6032073

3. Tool–Body Assimilation of Humanoid Robot Using a Neurodynamical System
Nishide, S.; Tani, J.; Takahashi, T.; Okuno, H.G.; Ogata, T.
Page(s): 139 - 149
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6095595

4. Are Robots Appropriate for Troublesome and Communicative Tasks in a City Environment?
Hayashi, K.; Shiomi, M.; Kanda, T.; Hagita, N.
Page(s): 150 - 160
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6111246

5. Brain-Like Emergent Spatial Processing
Juyang Weng; Luciw, M.
Page(s): 161 - 185
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6095596

IEEE Transactions on Computational Intelligence and AI in Games: Volume 4, Issue 2, 2012

1. N-Grams and the Last-Good-Reply Policy Applied in General Game Playing
Tak, M.J.W.; Winands, M.H.M.; Bjornsson, Y.
Page(s): 73 - 83
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6203383

2. A Discrete Evolutionary Model for Chess Players' Ratings
Fenner, T.; Levene, M.; Loizou, G.
Page(s): 84 - 93
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6168229

3. Evolving Multimodal Networks for Multitask Games
Schrum, J.; Miikkulainen, R.
Page(s): 94 - 111
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6179519

4. Bitwise-Parallel Reduction for Connection Tests
Browne, C.; Tavener, S.
Page(s): 112 - 119
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6185647

5. Information Set Monte Carlo Tree Search
Cowling, P.I.; Powley, E.J.; Whitehouse, D.
Page(s): 120 - 143
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6203567

6. Benchmarks for Grid-Based Pathfinding
Sturtevant, N.R.
Page(s): 144 - 148
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6194296

IEEE Transactions on Evolutionary Computation: Volume 16, Issue 4, 2012

1. Solving Multicommodity Capacitated Network Design Problems Using Multiobjective Evolutionary Algorithms
Kleeman, M. P.; Seibert, B. A.; Lamont, G. B.; Hopkinson, K. M.; Graham, S. R.
Page(s): 449 - 471
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6151105


2. An Integrated Neuroevolutionary Approach to Reactive Control and High-Level Strategy
Kohl, N.; Miikkulainen, R.
Page(s): 472 - 488
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6151106


3. A Process Algebra Genetic Algorithm
Karaman, S.; Shima, T.; Frazzoli, E.
Page(s): 489 - 503
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6045330


4. Using the Averaged Hausdorff Distance as a Performance Measure in Evolutionary Multiobjective Optimization
Schutze, O.; Esquivel, X.; Lara, A.; Coello, C. A. C.
Page(s): 504 - 522
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6151115


5. Promoting Creative Design in Interactive Evolutionary Computation
Kowaliw, T.; Dorin, A.; McCormack, J.
Page(s): 523 - 536
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6151108


6. Effects of Iterated Interactions in Multiplayer Spatial Evolutionary Games
Chiong, R.; Kirley, M.
Page(s): 537 - 555
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6151098


7. A General Framework of Multipopulation Methods With Clustering in Undetectable Dynamic Environments
Li, C.; Yang, S.
Page(s): 556 - 577
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6151109


8. On the Design of Constraint Covariance Matrix Self-Adaptation Evolution Strategies Including a Cardinality Constraint
Beyer, H.-G.; Finck, S.
Page(s): 578 - 596
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6151095