If complexity is your problem, learning classifier systems (LCSs) may offer a solution. These
rule‐based, multifaceted, machine learning algorithms originated and have evolved in the …

Broadly conceived as computational models of cognition and tools for modeling complex
adaptive systems, later extended for use in adaptive robotics, and today also applied to …

Für die Lösung von Optimierungsproblemen mit evolutionären Algorithmen stattet dieses
Buch seine Leser sowohl mit den gängigen Standardalgorithmen als auch mit den …

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 …

This chapter aims to give an overview of recent applications of Evolutionary Machine
Learning (EML) to control including opportunities and challenges. Control is at the heart of …

This paper presents an emotion inspired adaptive path planning approach for autonomous
robotic navigation. Ideally a robotic navigation system should adapt its path planning and …

For artificial entities to achieve true autonomy and display complex lifelike behavior, they will
need to exploit appropriate adaptable learning algorithms. In this context adaptability implies …

Biological organisms exist within environments in which complex nonlinear dynamics are
ubiquitous. They are coupled to these environments via their own complex dynamical …

For artificial entities to achieve true autonomy and display complex life-like behaviour they
will need to exploit appropriate adaptable learning algorithms. In this sense adaptability …

Most research in the held of learning classifier systems today concentrates on the accuracy-
based XCS. This paper presents initial results from an extension of XCS that operates in …