More than a decade after the first extensive overview on parameter control, we revisit the
field and present a survey of the state-of-the-art. We briefly summarize the development of …

The two major groups representing biologically inspired algorithms are swarm intelligence
(SI) and evolutionary computation (EC). Both SI and EC share common features such as the …

This chapter pursues the insight that large language models (LLMs) trained to generate
code can vastly improve the effectiveness of mutation operators applied to programs in …

This is the second edition of our 2003 book. It is primarily a book for lecturers and graduate
and undergraduate students. To this group the book offers a thorough introduction to …

In this paper we present a conceptual framework for parameter tuning, provide a survey of
tuning methods, and discuss related methodological issues. The framework is based on a …

Nowadays, optimization problems are solved through meta-heuristic algorithms based on
stochastic search approaches borrowed from mimicking natural phenomena …

Decomposition-based multiobjective evolutionary algorithms (MOEAs) decompose a
multiobjective optimization problem into a set of scalar objective subproblems and solve …

Evolution strategies are a class of evolutionary algorithms for black-box optimization and
achieve state-of-the-art performance on many benchmarks and real-world applications …

Evolutionary computation (EC) has been widely applied to biological and biomedical data.
The practice of EC involves the tuning of many parameters, such as population size …

The fields of machine meta-learning and hyper-heuristic optimisation have developed
mostly independently of each other, although evolutionary algorithms (particularly genetic …