Surrogate-based optimization, nature-inspired metaheuristics, and hybrid combinations
have become state of the art in algorithm design for solving real-world optimization …

Automated algorithm design has attracted increasing research attention recently in the
evolutionary computation community. The main design decisions include selection …

This work uses genetic programming to explore the space of continuous optimisers, with the
goal of discovering novel ways of doing optimisation. In order to keep the search space …

This work uses Push GP to automatically design both local and population-based optimisers
for continuous-valued problems. The optimisers are trained on a single function optimisation …

This work uses genetic programming to explore the design space of local optimisation
algorithms. Optimisers are expressed in the Push programming language, a stack-based …

This paper details an investigation of the extent to which performance can be improved for
the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) by tuning the selection of …

One promising method of improving Evolutionary Algorithm (EA) performance is to improve
its fine tuning capabilities by using an additional local optimization operator in the …

There have been many different forms of evolution-ary algorithms (EAs) designed by
humans over the past 50 years, with many variants optimized for specific classes of …

As computing power grows, the automated specialization and design of evolutionary
algorithms (EAs) to tune their performance to individual problem classes becomes more …

• Implement five types of GP (tree GP, linear GP, canonical Cartesian GP, Stack GP, and
Grammatical Evolution) in hyper-heuristics for evolving black-box search algorithms for …