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Evolutionary Computation in Economics and Finance

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Cover of 'Evolutionary Computation in Economics and Finance'

Table of Contents

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    Book Overview
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    Chapter 1 Evolutionary Computation in Economics and Finance: An Overview of the Book
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    Chapter 2 Playing Games with Genetic Algorithms
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    Chapter 3 Genetic Algorithm Learning and Economic Evolution
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    Chapter 4 Using Symbolic Regression to Infer Strategies from Experimental Data
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    Chapter 5 The Efficiency of an Artificial Double Auction Stock Market with Neural Learning Agents
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    Chapter 6 On AIE-ASM: Software to Simulate Artificial Stock Markets with Genetic Programming
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    Chapter 7 Exchange Rate Volatility in the Artificial Foreign Exchange Market
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    Chapter 8 Using an Artificial Market Approach to Analyze Exchange Rate Scenarios
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    Chapter 9 Emulating Trade in Emissions Permits: An Application of Genetic Algorithms
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    Chapter 10 Cooperative Computation with Market Mechanism
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    Chapter 11 Hysteresis in an Evolutionary Labor Market with Adaptive Search
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    Chapter 12 Computable Learning, Neural Networks and Institutions
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    Chapter 13 On Two Types of GA-Learning
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    Chapter 14 Evolutionary Computation and Economic Models: Sensitivity and Unintended Consequences
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    Chapter 15 Tinkering with Genetic Algorithms: Forecasting and Data Mining in Finance and Economics
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    Chapter 16 Forecasting Ability But No Profitability: An Empirical Evaluation of Genetic Algorithm-Optimised Technical Trading Rules
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    Chapter 17 Evolutionary Induction of Trading Models
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    Chapter 18 Optimizing Technical Trading Strategies with Split Search Genetic Algorithms
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    Chapter 19 GP Forecasts of Stock Prices for Profitable Trading
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    Chapter 20 Option Pricing Via Genetic Programming
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    Chapter 21 Evolutionary Computation in Option Pricing: Determining Implied Volatilities Based on American Put Options
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    Chapter 22 Evolutionary Computation in Economics and Finance: A Bibliography
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Citations

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Title
Evolutionary Computation in Economics and Finance
Published by
Physica-Verlag HD, November 2013
DOI 10.1007/978-3-7908-1784-3
ISBNs
978-3-79-082512-1, 978-3-79-081784-3
Editors

Chen, Shu-Heng

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 4 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 25%
Unknown 3 75%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 25%
Student > Doctoral Student 1 25%
Researcher 1 25%
Professor > Associate Professor 1 25%
Readers by discipline Count As %
Unspecified 1 25%
Computer Science 1 25%
Economics, Econometrics and Finance 1 25%
Physics and Astronomy 1 25%