Abstract Gene Expression Programming (GEP) is a popular and established evolutionary
algorithm for automatic generation of computer programs. In recent decades, GEP has …

Cartesian Genetic Programming (CGP) is a well-known form of Genetic Programming
developed by Julian Miller in 1999-2000. In its classic form, it uses a very simple integer …

Nature is phenomenal. The achievements in, for example, evolution are everywhere to be
seen: complexity, resilience, inventive solutions and beauty. Evolvable Hardware (EH) is a …

Cartesian genetic programming, a well-established method of genetic programming, is
approximately 20 years old. It represents solutions to computational problems as graphs. Its …

We describe a broad class of problems, called “uncompromising problems,” which are
characterized by the requirement that solutions must perform optimally on each of many test …

We present the results of a community survey regarding genetic programming benchmark
practices. Analysis shows broad consensus that improvement is needed in problem …

In this paper, a novel self-learning gene expression programming (GEP) methodology
named SL-GEP is proposed to improve the search accuracy and efficiency of GEP. In …

Geometric semantic operators are new and promising genetic operators for genetic
programming. They have the property of inducing a unimodal error surface for any …

A fast learning neuroevolutionary algorithm for both feedforward and recurrent networks is
proposed. The method is inspired by the well known and highly effective Cartesian genetic …

Graph representations promise several desirable properties for genetic programming (GP);
multiple-output programs, natural representations of code reuse and, in many cases, an …