Summary Evolutionary Algorithms (EAs) are stochastic search algorithms inspired by the
process of neo-Darwinian evolution. The motivation for applying EAs to data mining is that …

Memory may be broadly regarded as information gained from past experience that is
available in the service of ongoing and future adaptive behavior. The biological …

Complex adaptive systems (cas)–systems that involve many components that adapt or learn
as they interact–are at the heart of important contemporary problems. The study of cas poses …

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 …

This paper argues that evolutionary thinking and modeling can contribute to the emerging
research on sustainability transitions and their management. Evolutionary theory provides a …

This paper assesses methods and components of formal evolutionary-economic modelling.
Methods are broadly classified into evolutionary game theory and selection dynamics …

Learning classifier systems (LCSs) are rule-based systems that automatically build their
ruleset. At the origin of Holland's work, LCSs were seen as a model of the emergence of …

Electronic fraud is highly lucrative, with estimates suggesting these crimes to be worth
millions of dollars annually. Because of its complex nature, electronic fraud detection is …

Urban road networks are an infrastructural key factor for modern cities. To facilitate an
efficient transportation of people and goods, it is crucial to optimise the networks' …

Abstract [Learning] Classifier systems are a kind of rule-based system with general
mechanisms for processing rules in parallel, for adaptive generation of new rules, and for …