Several methods to incorporate semantic awareness in genetic programming have been
proposed in the last few years. These methods cover fundamental parts of the evolutionary …

In genetic programming, a search algorithm is expected to produce a program that achieves
the desired final computation state (desired output). To reach that state, an executing …

Geometric semantic genetic programming (GP) has recently attracted much attention. The
key innovations are inducing a unimodal fitness landscape in the semantic space and …

Behavioral Program Synthesis with Genetic Programming Page 1 Studies in Computational
Intelligence 618 Krzysztof Krawiec Behavioral Program Synthesis with Genetic Programming …

Despite the empirical success of Genetic programming (GP) in various symbolic regression
applications, GP is not still known as a reliable problem-solving technique in this domain …

We propose a novel crossover operator for tree-based genetic programming, that produces
approximately geometric offspring. We empirically analyze certain aspects of geometry of …

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 …

This paper provides a structured, unified, formal and empirical perspective on all geometric
semantic crossover operators proposed so far, including the exact geometric crossover by …

Abstract Semantics has become a key topic of research in Genetic Programming (GP).
Semantics refers to the outputs (behaviour) of a GP individual when this is run on a dataset …

The semantic geometric crossover (SGX) proposed by Moraglio et al. has achieved very
promising results and received great attention from researchers, but has a significant …