GECCO 2009

A big shout-out to the organizers and participants of the 2009 Genetic and Evolutionary Computation Conference.
I really missed not being there. Can’t wait to read the proceedings.

Dissertation of the Year Award

I won the 2009 doctoral dissertation of the year award from the College of Engineering (University of Toledo) for my dissertation titled “Grammatical Evolution based Data Mining for Network Intrusion Detection”. I defended it in April 2008.

Feeling great about it.

MLK Day on Monday, then Obama’s inauguration on Tuesday.

Image by BL1961 via Flickr

Martin Luther King’s Day on Monday, then Obama’s inauguration on Tuesday; could you have arranged it any better?

I am looking forward to Tuesday’s inauguration poem, which is authored (and will be recited) by the highly accomplished poet Elizabeth Alexander.

Search, Neutral Evolution and Mapping in Evolutionary Computing:2

Here is a pre-proof copy of my accepted paper: “Search, Neutral Evolution and Mapping in Evolutionary Computing: A Case Study of Grammatical Evolution”.

I would encourage you to read section X  (Analysis of related works) , to see its true implications.

I plan to do a series of posts on what this paper means for Evolutionary Computing, and to post some of the MATLAB code used in this work.

You might want to subscribe to my RSS feed.

“This paper is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author’s copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.”

Search, Neutral Evolution and Mapping in Evolutionary Computing: A Case Study of Grammatical Evolution

The above is the title of my paper that has been accepted by IEEE Trans. on Evolutionary Computing. My doctoral supervisor Dr Kaur is a coauthor of the paper.

Here is the abstract:

We present a new perspective of search in Evolutionary Computing (EC) by using a novel model for the analysis and visualization of genotype to phenotype maps. The model groups genes into quotient sets and shows their adjacencies. A unique quality of the quotient model is that it details geometric qualities of maps that are not otherwise easy to observe. The model shows how random mutations on genes make non-random phenotype preferences, based on the structure of a map. The interaction between such mutation-based preferences with fitness preferences is important for explaining population movements on neutral landscapes. We show the widespread applicability of our approach by applying it to different representations, encodings and problems including Grammatical Evolution (GE), Cartesian Genetic Programming, Parity and Majority Coding, OneMax, Needle-in-Haystack, Deceptive Trap and Hierarchical if-and-only-if. We also use the approach to address conflicting results in the neutral evolution literature and to analyze concepts relevant to neutral evolution including robustness, evolvability, tunneling and the relation between genetic form and function.

We use the model to develop theoretical results on how mapping and neutral evolution affects search in GE. We study the two phases of mapping in GE; these being transcription (i.e. unique identification of genes with integers), and translation (i.e. many-to-one mapping of genotypes to phenotypes). It is shown that translation and transcription schemes belong to equivalence classes, therefore the properties we derive for specific schemes are applicable to classes of schemes. We present a new perspective on population diversity. We specify conditions under which increasing degeneracy (by increasing codon size) or rearranging the rules of a grammar do not affect performance. It is shown that there is a barrier to nontrivial neutral evolution with the use of the natural transcription with modulo translation combination; a necessary but not sufficient condition for such evolution is at least three bits should change on mutation within a single codon. This barrier can be avoided by using Gray transcription. We empirically validate some findings.

This paper was originally written with a more modest scope limited to Grammatical Evolution (which was a central part of my dissertation). If it seems overachieving it is partly due to me and partly due to reviewer requests for a more general approach. The amount of work behind this paper is painful to even think of. I am not complaining though, the final product is certainly worth the effort and I am very satisfied at the quality.

Please email me if you want to see a draft copy.

Yes We Can! The affirmation of the collective.

Image via Wikipedia

In bio-inspired fields of Artificial Intelligence (such as neural networks, particle swarms and evolutionary computing), the methodology of performance improvement is based on using or networking a large collection of simple processing elements to achieve an emergent collective intelligence. The whole surpasses the sum of its parts.

Barack Obama, having been a community organizer, understands the power of this principle of bottom-up organic synthesis. With such arrangements, whether of people, neurons or cellular automata, the power is in the network.

Artificial Intelligence researchers know that such networks can have complicated dynamics with difficult to predict results. Nonetheless these fascinating networks being tried and tested mechanisms of nature doubtlessly have the capacity to deliver when they must.

Yes We Can!

Confessions of a research junkie (on squeezing the magic out of stuff)

As a researcher, your topic of interest has its magic.

You bond with your topic; get to know all there is to know about it, explore it from new perspectives, think about it day and night.

You get to the point where you know that what you know about your topic is known by less than a handful of the 6 billion people walking the planet.

You work on, looking for the break.

If it were that easy, someone else would have published it by now. So you toil on, working that new angle.

And then you hit the payload;

What a rush!

Sometimes it’s a deluge; other times it comes in sweet dribs; things gel out; you see the light. You OWN that topic.

It pushes you through the process of publishing your findings.

There is no magic left there. You move on. Find something else interesting. You hope, pray, work for the next rush.

Hopefully bigger; hopefully better.

My GECCO 2008 Conference Presentation

Image via Wikipedia

Genetic and Evolutionary Computing Conference (GECCO Atlanta, Georgia) 2008 is the best academic conference I have ever attended. There were lots of tutorials and workshops, many interesting papers and concepts.

I got to stay at Georgia Tech – What a nice university.

Unfortunately, I did not find time to see the sites in Atlanta as much as I wanted to. There was always something interesting going on in the conference.

Got to meet Poli, McPhee, Ryan, and many other stars of the Evolutionary Computing Community for the first time. Just that was worth the price of the conference.

Below are the slides of my presentation for the paper ” Using Quotient Graphs to Model Neutrality in Evolutionary Search”

presentation-for-gecco-2008

I Graduate!

Dr Wilson

Dr Wilson, Dr Chou

Dr Wilson, Dr Kim

Class of 2008

Dr Kaur, Dr Wilson, Dr Naganathan, Dr Smith

Being hooded by Dr Kaur

{Sentence} -> {Word} {Space} {Word} {Stop} -> Hello world!

Welcome to my blog!

I am just about finishing my PhD and that gives me some time to express my opinions and ideas on the science and engineering of intelligent machines. Intelligent machines are machines (or systems of machines) that show abilities we commonly associate with intelligence; these include the capacity to learn, reason, plan and derive and use knowledge from their environment towards achieving some goal.

I am using the term machine in the wide sense, i.e. a machine does not have to be mechanical, it just has to have some machinery (defined as ” A system of related elements that operate in a definable manner”). There we go, you really can’t go far into machine intelligence without considering grammars. Grammars are the rules governing the operations of machines. Grammars say what machines can legally do, how they are defined to operate. Grammars can be viewed from the perspectives of being guides and/or restrictions on the operations of machines. Grammars are everywhere, trust me.

You should have guessed by now that I am a fan of grammars. My doctoral dissertation is on applying Grammatical Evolution to the task of data mining network intrusion data. I have to defend it some time next month, and then I am done (I hope). See you around.