Memetic computing is a subject in computer science which considers complex structures
such as the combination of simple agents and memes, whose evolutionary interactions lead …

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

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 …

Memetic Algorithms (MAs) are computational intelligence structures combining multiple and
various operators in order to address optimization problems. The combination 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 …

Particle swarm optimization (PSO) has been shown as an effective tool for solving global
optimization problems. So far, most PSO algorithms use a single learning pattern for all …

This paper presents an investigation of a simple generic hyper-heuristic approach upon a
set of widely used constructive heuristics (graph coloring heuristics) in timetabling. Within …

Hyperheuristics can be defined to be heuristics which choose between heuristics in order to
solve a given optimisation problem. The main motivation behind the development of such …

Adaptation of parameters and operators represents one of the recent most important and
promising areas of research in evolutionary computations; it is a form of designing self …

The term “hyper-heuristics” is fairly new, although the notion has been hinted at in papers
from time to time since the 1960s (eg Crowston et al., 1963). The key idea is to devise new …