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Learning in Economics
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Learning in Economics

Learning in Economics

Analysis and Application of Genetic Algorithms

Thomas Riechmann

179 pages, parution le 01/04/2001

Résumé

The book is dedicated to the use of genetic algorithms in theoretical economic research. Genetic algorithms offer the chance of overcoming the limitations traditional mathematical tractability puts on economic research and thus open new horzions for economic theory. The book reveals close relationships between the theory of economic learning via genetic algorithms, dynamic game theory, and evolutionary economics.

Genetic algorithms are here introduced as metaphors for processes of social and individual learning in economics. The book gives a simple description of the basic structures of economic genetic algorithms, followed by an in-depth analysis of their working principles. Several well-known economic models are reconstructed to incorporate genetic algorithms. Genetic algorithms thus help to find genuinely new results of well-known economic problems.

Contents

Introduction

  • Introduction
  • The Core topics: learning and computational economics
  • An exemplary introduction to structure and application of genetic algorithms on economic research
General analysis of genetic algorithms
  • Methods fot the general analysis of genetic algorithms as economic leraning techniques
  • Statistical aspects of the analysis of economic genetic algorithms
Economic applications and technical varaiations
  • Modifications: election and meta-learning
  • Extensions: variable time horizon of selection
  • Algorithms with real valued coding
  • Multi population algorithm
  • final remarks

Caractéristiques techniques

  PAPIER
Éditeur(s) Springer
Auteur(s) Thomas Riechmann
Parution 01/04/2001
Nb. de pages 179
Format 15,5 x 23,5
Couverture Broché
Poids 293g
Intérieur Noir et Blanc
EAN13 9783790813845

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