Hyper-heuristics have emerged as a way to raise the level of generality of search techniques
for computational search problems. This is in contrast to many approaches, which represent …

Hyper-heuristics are search techniques for selecting, generating, and sequencing (meta)-
heuristics to solve challenging optimization problems. They differ from traditional (meta) …

A hyper-heuristic algorithm is a general solution framework that adaptively selects the
optimizer to address complex problems. A classical hyper-heuristic framework consists of …

During the past few years, research in applying machine learning (ML) to design efficient,
effective, and robust metaheuristics has become increasingly popular. Many of those …

Hyper-heuristics comprise a set of approaches that aim to automate the development of
computational search methodologies. This chapter overviews previous categorisations of …

Educational timetabling is an ongoing challenging administrative task that is required in
most academic institutions. This is mainly due to a large number of constraints and …

Hyper-heuristics is a fairly recent technique that aims at effectively solving various real-world
optimization problems. This is the first book on hyper-heuristics, and aims to bring together …

Genetic programming (GP) is a powerful evolutionary algorithm that has been widely used
for solving many real-world optimization problems. However, traditional GP can only solve a …

Task allocation is a crucial issue of mobile crowdsensing. The existing crowdsensing
systems normally select the optimal participants giving no consideration to the sudden …

Abstract Gene Expression Programming (GEP) is a popular and established evolutionary
algorithm for automatic generation of computer programs. In recent decades, GEP has …