This e-book is devoted to global optimization algorithms, which are methods to find optimal
solutions for given problems. It especially focuses on Evolutionary Computation by …

Grammar formalisms are one of the key representation structures in Computer Science. So it
is not surprising that they have also become important as a method for formalizing …

We investigate the effects of semantically-based crossover operators in genetic
programming, applied to real-valued symbolic regression problems. We propose two new …

In this paper, we apply the ideas from [2] to investigate the effect of some semantic based
guidance to the crossover operator of GP. We conduct a series of experiments on a family of …

This paper describes Christiansen grammar evolution (CGE), a new evolutionary automatic
programming algorithm that extends standard grammar evolution (GE) by replacing context …

Machine learning methods are growing in relevance for biometrics and personal information
processing in domains such as forensics, e-health, recruitment, and e-learning. In these …

Genetic Programming (GP) is an heuristic method that can be applied to many Machine
Learning, Optimization and Engineering problems. In particular, it has been widely used in …

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 …

Locality has long been seen as a crucial property for the efficiency of Evolutionary
Algorithms in general, and Genetic Programming (GP) in particular. A number of studies …

This paper examines the impact of semantic control on the ability of Genetic Programming
(GP) to generalise via a semantic based crossover operator (Semantic Similarity based …