Information Technology Reference
In-Depth Information
3
The Basic Gene
Expression Algorithm
The fundamental steps of the gene expression algorithm (GEA) are
schematically represented in Figure 3.1. The process begins with the random
generation of the chromosomes of a certain number of individuals (the initial
population). Then these chromosomes are expressed and the fitness of each
individual is evaluated against a set of fitness cases (also called selection envi-
ronment which, in fact, is the input to a problem). The individuals are then
selected according to their fitness (their performance in that particular envi-
ronment) to reproduce with modification, leaving progeny with new traits.
These new individuals are, in their turn, subjected to the same developmental
process: expression of the genomes, confrontation of the selection environ-
ment, selection, and reproduction with modification. The process is repeated
for a certain number of generations or until a good solution has been found.
In this chapter, we will analyze with great detail all the fundamental steps
of this evolutionary algorithm, starting with the random generation of the
chromosomes of all the individuals of the initial population and finishing
with their selection and reproduction with modification, which obviously
leads to the creation of the new individuals of the next generation. The goal
consists not only in studying the logistics of the gene expression algorithm
but also in understanding why and how populations of computer programs
evolve from generation to generation, becoming better and better solutions
to the problem at hand.
We have already seen that populations of entities, be they organisms or
computer programs, evolve because individuals are reproduced with modifi-
cation, giving rise to genetic diversity, which is the raw material of evolu-
tion. This genetic diversity is the basis for a differential selection and, there-
fore, plays a central role in evolution. Thus, we are going to analyze thor-
oughly the mechanisms and effects of all the agents of genetic diversity - the
genetic operators. Each genetic operator is going to be used to solve the
Search WWH ::




Custom Search