Genetic Programming was first introduced by Koza using tree representation together with a crossover technique in which random sub-branches of the parents’ trees are swapped to create the offspring. Later Miller and Thomson introduced Cartesian Genetic Programming, which uses directed graphs as a representation to replace the tree structures originally introduced by Koza. Cartesian Genetic Prog...

In this paper, we introduce a new grammar guided genetic programming system called tree-adjoining grammar guided genetic programming (TAG3P+), where tree-adjoining grammars (TAGs) are used as means to set language bias for genetic programming. We show that the capability of TAGs in handling context-sensitive information and categories can be useful to set a language bias that cannot be specifie...

Motivated by biological inspiration and the issue of code disruption, we develop a new form of LGP called Parallel LGP (PLGP). PLGP programs consists of n lists of instructions. These lists are executed in parallel, after which the resulting vectors are combined to produce program output. PGLP limits the disruptive effects of crossover and mutation, which allows PLGP to significantly outperform...

Biometric systems suffer from some drawbacks: a biometric system can provide in general good performances except with some individuals as its performance depends highly on the quality of the capture... One solution to solve some of these problems is to use multibiometrics where different biometric systems are combined together (multiple captures of the same biometric modality, multiple feature ...

Biochemical pathways are the fundamental structures of biological representations. Biological representations are the fundamental targets of natural evolution. Evolution is the fundamental principle behind genetic programming. Could biological representations be useful to genetic programming? Are biochemical pathways a suitable representation for programs? These are the fundamental questions ad...

With the help of schemas and genetic programming we describe systems which • interact with the real world • make theories about the consequences of their actions and • dynamically adjust inductive bias. We present experimental data related to learning geometric concepts and moving a block in a microworld.

Genetic Programming is increasing in popularity as the basis for a wide range of learning algorithms. However, the technique has to date only been successfully applied to modest tasks because of the performance overheads of evolving a large number of data structures, many of which do not correspond to a valid program. We address this problem directly and demonstrate how the evolutionary process...

This paper formally introduces Recurrent Cartesian Genetic Programming (RCGP), an extension to Cartesian Genetic Programming (CGP) which allows recurrent connections. The presence of recurrent connections enables RCGP to be successfully applied to partially observable tasks. It is found that RCGP significantly outperforms CGP on two partially observable tasks: artificial ant and sunspot predict...