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Simulated Evolution and Learning

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Cover of 'Simulated Evolution and Learning'

Table of Contents

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    Book Overview
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    Chapter 1 Natural Computation
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    Chapter 2 Multiple Lagrange Multiplier Method for Constrained Evolutionary Optimization
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    Chapter 3 Robust Evolution Strategies
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    Chapter 4 Hybrid Genetic Algorithm for Solving the p -Median Problem
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    Chapter 5 Correction of Reflection Lines Using Genetic Algorithms
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    Chapter 6 Adaptation under Changing Environments with Various Rates of Inheritance of Acquired Characters
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    Chapter 7 Dynamic Control of Adaptive Parameters in Evolutionary Programming
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    Chapter 8 Information Operator Scheduling by Genetic Algorithms
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    Chapter 9 Solving Radial Topology Constrained Problems with Evolutionary Algorithms
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    Chapter 10 Automating Space Allocation in Higher Education
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    Chapter 11 Application of Genetic Algorithm and k-Nearest Neighbour Method in Medical Fraud Detection
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    Chapter 12 Evolution of Reference Sets in Nearest Neighbor Classification
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    Chapter 13 Investigation of a Cellular Genetic Algorithm that Mimics Landscape Ecology
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    Chapter 14 Quantifying Neighborhood Preservation: Joint Properties of Evolutionary and Unsupervised Neural Learning
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    Chapter 15 Neural Networks and Evolutionary Algorithms for the Prediction of Thermodynamic Properties for Chemical Engineering
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    Chapter 16 Evolving FPGA Based Cellular Automata
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    Chapter 17 Asynchronous Island Parallel GA Using Multiform Subpopulations
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    Chapter 18 Multiple Sequence Alignment Using Parallel Genetic Algorithms
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    Chapter 19 Evolving Logic Programs to Classify Chess-Endgame Positions
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    Chapter 20 Genetic Programming with Active Data Selection
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    Chapter 21 Evolutionary Programming-Based Uni-vector Field Method for Fast Mobile Robot Navigation
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    Chapter 22 Evolution with Learning Adaptive Functions
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    Chapter 23 Modelling Plant Breeding Programs as Search Strategies on a Complex Response Surface
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    Chapter 24 Generating Equations with Genetic Programming for Control of a Movable Inverted Pendulum
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    Chapter 25 Simulated Evolution and Learning
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    Chapter 26 Reinforcement Learning: Past, Present and Future
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    Chapter 27 A Reinforcement Learning with Condition Reduced Fuzz Rules
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    Chapter 28 Generality and Conciseness of Submodels in Hierarchical Fuzzy Modeling
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    Chapter 29 Using Evolutionary Programming to Optimize the Allocation of Surveillance Assets
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    Chapter 30 Applying the Evolutionary Neural Networks with Genetic Algorithms to Control a Rolling Inverted Pendulum
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    Chapter 31 Evolving Cooperative Actions Among Heterogeneous Agents by an Evolutionary Programming Method
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    Chapter 32 Cooperative Works for Welfare Agent Robot and Human Using Chaotic Evolutionary Computation
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    Chapter 33 Evolutionary Computation for Intelligent Agents Based on Chaotic Retrieval and Soft DNA
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    Chapter 34 A Study of Bayesian Clustering of a Document Set Based on GA
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    Chapter 35 An Evolutionary Approach in Quantitative Spectroscopy
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    Chapter 36 Evolutionary Recognition of Features from CAD Data
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    Chapter 37 Modeling Strategies as Generous and Greedy in Prisoner’s Dilemma Like Games
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    Chapter 38 Using Genetic Algorithms to Simulate the Evolution of an Oligopoly Game
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    Chapter 39 An Evolutionary Study on Cooperation in N-person Iterated Prisoner’s Dilemma Game
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    Chapter 40 Simulating a N-person Multi-stage Game for Making a State
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    Chapter 41 Learning from Linguistic Rules and Rule Extraction for Function Approximation by Neural Networks
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    Chapter 42 Can a Niching Method Locate Multiple Attractors Embedded in the Hopfield Network?
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    Chapter 43 Time Series Prediction by Using Negatively Correlated Neural Networks
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    Chapter 44 Animating the Evolution Process of Genetic Algorithms
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    Chapter 45 Analysis on the Island Model Parallel Genetic Algorithms for the Genetic Drifts
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    Chapter 46 A Paradox of Neural Encoders and Decoders or Why Don’t We Talk Backwards?
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    Chapter 47 Continuous Optimization Using Elite Genetic Algorithms With Adaptive Mutations
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    Chapter 48 Evolutionary Systems Applied to the Synthesis of a CPU Controller
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    Chapter 49 Novel Models in Evolutionary Designing
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    Chapter 50 Co-evolution, Determinism and Robustness
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    Chapter 51 Co-operative Evolution of a Neural Classifier and Feature Subset
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    Chapter 52 Optimal Power Flow Method Using Evolutionary Programming
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    Chapter 53 Grammatical Development of Evolutionary Modular Neural Networks
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    Chapter 54 Hybridized Neural Network and Genetic Algorithms for Solving Nonlinear Integer Programming Problem
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    Chapter 55 Evolution of Gene Coordination Networks
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    Chapter 56 Simulated Evolution and Learning
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    Chapter 57 A Model of Mutual Associative Memory for Simulations of Evolution and Learning
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    Chapter 58 The Application of Cellular Automata to the Consumer’s Theory: Simulating a Duopolistic Market
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    Chapter 59 Object-Oriented Genetic Algorithm Based Artificial Neural Network for Load Forecasting
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Title
Simulated Evolution and Learning
Published by
Springer Science & Business Media, May 1999
DOI 10.1007/3-540-48873-1
ISBNs
978-3-54-065907-5, 978-3-54-048873-6
Editors

McKay, Bob, Yao, Xin, Newton, Charles S., Kim, Jong-Hwan, Furuhashi, Takeshi

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The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
 Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 6 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 100%
Researcher 1 17%
Readers by discipline Count As %
Engineering 4 67%
Computer Science 2 33%
Mathematics 1 17%

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