Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This book gathers together much of the author’s work – both old and new - to explore a number of the key increases in complexity seen in the natural world, seeking to explain each of them purely in terms of the features of fitness landscapes. In a very straightforward manner, the book introduces basic concepts to help readers follow the main ideas. By using variations of the NK model and including the concept of the Baldwin effect, the author presents new abstract models that are able to explain why sources of evolutionary innovation (genomes, symbiosis, sex, chromosomes, multicellularity) have been selected for and hence how complexity has increased over time in some lineages.

Computational complexity. --- Evolutionary biology. --- Machine learning. --- Statistical physics. --- Dynamical systems. --- Complexity. --- Evolutionary Biology. --- Machine Learning. --- Complex Systems. --- Dynamical systems --- Kinetics --- Mathematics --- Mechanics, Analytic --- Force and energy --- Mechanics --- Physics --- Statics --- Mathematical statistics --- Learning, Machine --- Artificial intelligence --- Machine theory --- Animal evolution --- Animals --- Biological evolution --- Darwinism --- Evolutionary biology --- Evolutionary science --- Origin of species --- Biology --- Evolution --- Biological fitness --- Homoplasy --- Natural selection --- Phylogeny --- Complexity, Computational --- Electronic data processing --- Statistical methods --- Bioinformatics. --- Bio-informatics --- Biological informatics --- Information science --- Computational biology --- Systems biology --- Data processing

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This volume brings together recent theoretical work in Learning Classifier Systems (LCS), which is a Machine Learning technique combining Genetic Algorithms and Reinforcement Learning. It includes self-contained background chapters on related fields (reinforcement learning and evolutionary computation) tailored for a classifier systems audience and written by acknowledged authorities in their area - as well as a relevant historical original work by John Holland.

Machine learning. --- Genetic algorithms. --- Reinforcement learning. --- Apprentissage automatique --- Algorithmes génétiques --- Apprentissage par renforcement (Intelligence artificielle) --- Engineering. --- Artificial intelligence. --- Bioinformatics. --- Mathematics. --- Engineering mathematics. --- Appl.Mathematics/Computational Methods of Engineering. --- Artificial Intelligence (incl. Robotics). --- Applications of Mathematics. --- Machine learning --- Genetic algorithms --- Reinforcement learning --- Applied Mathematics --- Computer Science --- Civil Engineering --- Engineering & Applied Sciences --- Civil & Environmental Engineering --- GAs (Algorithms) --- Genetic searches (Algorithms) --- Learning, Machine --- Engineering --- Engineering analysis --- Math --- Bio-informatics --- Biological informatics --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Construction --- Mathematics --- Applied mathematics. --- Mathematical and Computational Engineering. --- Artificial Intelligence. --- Mathematical analysis --- Biology --- Information science --- Computational biology --- Systems biology --- Science --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Data processing

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Mathematics --- Classical mechanics. Field theory --- Statistical physics --- Evolution. Phylogeny --- Programming --- Computer. Automation --- complexiteit --- statistiek --- programmeren (informatica) --- Europees recht --- fysica --- dynamica

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This volume brings together recent theoretical work in Learning Classifier Systems (LCS), which is a Machine Learning technique combining Genetic Algorithms and Reinforcement Learning. It includes self-contained background chapters on related fields (reinforcement learning and evolutionary computation) tailored for a classifier systems audience and written by acknowledged authorities in their area - as well as a relevant historical original work by John Holland.

Mathematics --- Biomathematics. Biometry. Biostatistics --- Engineering sciences. Technology --- Artificial intelligence. Robotics. Simulation. Graphics --- analyse (wiskunde) --- toegepaste wiskunde --- bio-informatica --- biometrie --- KI (kunstmatige intelligentie) --- ingenieurswetenschappen --- robots

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Just over thirty years after Holland first presented the outline for Learning Classifier System paradigm, the ability of LCS to solve complex real-world problems is becoming clear. In particular, their capability for rule induction in data mining has sparked renewed interest in LCS. This book brings together work by a number of individuals who are demonstrating their good performance in a variety of domains. The first contribution is arranged as follows: Firstly, the main forms of LCS are described in some detail. A number of historical uses of LCS in data mining are then reviewed before an overview of the rest of the volume is presented. The rest of this book describes recent research on the use of LCS in the main areas of machine learning data mining: classification, clustering, time-series and numerical prediction, feature selection, ensembles, and knowledge discovery.

Engineering sciences. Technology --- Artificial intelligence. Robotics. Simulation. Graphics --- analyse (wiskunde) --- data mining --- ingenieurswetenschappen --- robots