Novelty search is a state-of-the-art evolutionary approach that promotes behavioural novelty
instead of pursuing a static objective. Along with a large number of successful applications …

Novelty search and related diversity-driven algorithms provide a promising approach to
overcoming deception in complex domains. The behavior characterization (BC) is a critical …

Research on semantics in Genetic Programming (GP) has increased over the last number of
years. Results in this area clearly indicate that its use in GP considerably increases …

Genetic Improvement (GI) focuses on the development of evolutionary methods to automate
software engineering tasks, such as performance improvement or software bugs removal …

Cooperative coevolutionary algorithms (CCEAs) rely on multiple coevolving populations for
the evolution of solutions composed of coadapted components. CCEAs enable, for instance …

Novelty search (NS) is an open-ended evolutionary algorithm that eliminates the need for an
explicit objective function. Instead, NS focuses selective pressure on the search for novel …

The objective function is the core element in most search algorithms that are used to solve
engineering and scientific problems, referred to as the fitness function in evolutionary …

Novelty Search (NS) is a unique approach towards search and optimization, where an
explicit objective function is replaced by a measure of solution novelty. However, NS has …

Grammatical Evolution is an evolutionary algorithm that can evolve complete programs
using a Backus Naur form grammar as a plug-in component to describe the output …

Bloat is one of the most interesting theoretical problems in genetic programming (GP), and
one of the most important pragmatic limitations in the development of real-world GP …