The field of natural computing has been the focus of a substantial research effort in recent
decades. One particular strand of this concerns the development of computational …

Structured grammatical evolution (SGE) is a new genotypic representation for grammatical
evolution (GE). It comprises a hierarchical organization of the genes, where each locus is …

Grammatical Evolution (GE) is a Evolutionary Algorithm (EA) that takes inspiration from the
biological evolutionary process to search for solutions to problems. This chapter gives a brief …

We present an analysis of the genotype-phenotype map in Grammatical Evolution (GE). The
standard map adopted in GE is a depth-first expansion of the non-terminal symbols during …

In machine learning, reducing the complexity of a model can help to improve its
computational efficiency and avoid overfitting. In genetic programming (GP), the model …

Evolutionary algorithms are a competent nature-inspired approach for complex
computational problem solving. One recent development is Grammatical Evolution, a …

The term “autonomic networking” refers to network-level software systems capable of self-
management, according to the principles outlined by the Autonomic Computing initiative …

Research extending the capabilities of the well-known evolutionary-algorithm (EA) of
Grammatical Evolution (GE) is presented. GE essentially describes a software component …

The act of going from genotype to phenotype in Grammatical Evolution requires the
application of a map** process. This map** process works in conjunction with a …

Traditionally, reducing complexity in Machine Learning promises benefits such as less
overfitting. However, complexity control in Genetic Programming (GP) often means reducing …