A New Schema and Landscape for Programs

Dr Kaur and I sent a paper on a new schema and landscape for IEEE TEC peer review two weeks ago.

In the paper we showcase the new schema and landscape analyses by applying it to the Santa Fe ant problem. This caused us to discover for the first time the relationship between program structures and program fitness. Traditionally the Santa Fe ant problem is well known for presenting a random fitness relationship when analyzed by any other method.

We also show for the first time the systematic approaches to fitness improvement that programs make during genetic programming runs, thereby showing that the process is very different from what a random search does. We test a new variation and representation method that were designed based on our findings and obtained more efficient evolutionary search.

Please send me an email if you would like a copy for personal review.

We are currently undecided on whether we should post the pre-review paper to Arxiv.org. Any advice?


Making It To The Most Read Articles Lists in 2009

The Paper “Search, Neutral Evolution, and Mapping in Evolutionary Computing: A Case Study of Grammatical Evolution” Wilson, D.   Kaur, D.,  appeared in the July 2009 Top 10 Downloads of the IEEE Transactions on Evolutionary Computing ranked #1.

It also appeared (ranked #27) in the Top 100 Downloads of the entire IEEExplore site for July 2009!

Not bad!

Some ways you can go wrong with Evol. Comp. II

Misunderstanding Randomness

There are two aspects of randomness in Evolutionary computing that are frequently misunderstood . The first issues is the assumption that the effects of random mutations are always random. No that is not a typo, the effects of random mutation are usually not random but are coordinated into nonrandom distributions based on how genes map to their measured behavior (aka their phenotypes).

A good analogy to explain this concept is the bean machine. As explained in Wikipedia, the bean machine

Bean Machine

Bean Machine

was invented to demonstrate the law of error and the normal distribution. The machine consists of a vertical board with interleaved rows of pins. Balls, dropped from the top, bounce in random directions on hitting the pins. Not withstanding their random horizontal motions on descent, the balls settle at the bottom of the machine in an approximately normal distribution.

The second misunderstood aspect of randomness has to do with the way it is measured in populations. Some researchers measure the amount of diversity in a population by summing the variance of genetic (or allelic) values for all locations on genomes.Based on the evolutionary landscape this measure can overstate the search potential of a population. A population can be effectively converged (i.e. all of the genomes can have the same fitness and all can be searching the representation space the same way) without there being a low variance between their gene values.

I have a forthcoming  paper (accepted by IEEE Transactions on Evolutionary Computing) , which among other things, looks at preferred directions of motion due to random mutation as well as randomness in evolutionary populations. I will blog on this further when it is published.

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