Learning Genetic Algorithms, Robust Gene Expression Programming

My thesis is on a new form of Gene Expression Programming that I've named Robust Gene Expression Programming (RGEP). It is designed to be simpler and more robust than its closest relatives, without losing generality or performance. In this post I'm just going to describe each part of RGEP without a complete explanation.

RGEP consists of the following stages: a single initialization step, followed by n generations consisting of gene expression, evaluation, selection, point mutation, rotation, one point crossover, and two point crossover.

The population consists of bit vectors of some fixed length. Initializing them is easy- they are completely random. All individuals are valid, so there is no need to do any checking while creating individuals.

Gene expression consists of splitting the bit vectors (which are symbol lists where the symbols can only be 1 or 0) into fixed size groups called codons. Each codon is expressed into a symbol, forming the raw symbol lists. This can then be edited/expressed into a tree using the postfix notation from the last post. The resulting tree can then have its fitness determined.

Selection is a simple tournament selection with 2 individuals per tournament and elitism.

Point mutation is just like in a GA.

Rotation rotates an individual just like a wrap around shift in a register. The rotation point must be a multiple of the codon size so that the rotation occur on codons, not bits. This prevents rotation from being terribly disruptive.

One point crossover is just like in a GA, and two point crossover is just like one point crossover except we chose two random points and exchange with respect to both.

So- that was a very short description missing details on the decoding process. I may go into much more detail on this in later posts, as well as other topics in GAs and Evolutionary Algorithms.