For Your Viewing Pleasure

Below is the first of a series of lectures on Evolution, Ecology and Behavior by Professor Stearns of Yale University.

It has a lot of material that should be of interest to anyone interested in Evolutionary Computing. The fourth video in the series is on neutral evolution and genetic drift.

This series is going to make up a good portion of my holiday viewing.

Extending the Phenotype in Evolutionary Computing

In his very readable book “The Extended Phenotype“, Dawkins defined the “Extended Phenotype” as the effects of a gene when those effects are not regarded as being confined to the individual body in which the gene sits. He argues against the arbitrariness of limiting the applicability of phenotypes only to expressions of an organism’s genes in its own body.

In Evolutionary Computing, the term “phenotype” is usually reserved for a final representation of a genome along the process of transformation pursuant to being assigned fitness. In my paper (on search, neutral evolution and mapping in evolutionary computing), we used a new approach similar to Dawkins’ extension. We relaxed the generally used definition of a phenotype to include intermediate representations of a genome along its transformation to a fitness value. This means a Genotype to Phenotype map can be a map to some intermediate or final form.

The idea of extended phenotypes is controversial in biological circles; however relaxing the definition of a phenotype proves to be very useful for analyzing evolutionary computation because it gives a single general framework for analyzing encodings, representations and problems.

As a result of the relaxation we were able to analyze encodings (e.g. parity coding) and representations (e.g. grammatical evolution) without reference to a specific problem. we also  used analyzed specific problems (e.g. OneMax) and problems using a particular encoding or representation (e.g. OneMax using parity coding, and boolean parity using cartesian genetic programming). It is difficult to find any other approach that can deal with encoding, representations and problems within the same framework.


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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.

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