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Genetic Programming Theory and Practice XVII

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Cover of 'Genetic Programming Theory and Practice XVII'

Table of Contents

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    Book Overview
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    Chapter 1 Characterizing the Effects of Random Subsampling on Lexicase Selection
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    Chapter 2 It Is Time for New Perspectives on How to Fight Bloat in GP
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    Chapter 3 Explorations of the Semantic Learning Machine Neuroevolution Algorithm: Dynamic Training Data Use, Ensemble Construction Methods, and Deep Learning Perspectives
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    Chapter 4 Can Genetic Programming Perform Explainable Machine Learning for Bioinformatics?
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    Chapter 5 Symbolic Regression by Exhaustive Search: Reducing the Search Space Using Syntactical Constraints and Efficient Semantic Structure Deduplication
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    Chapter 6 Temporal Memory Sharing in Visual Reinforcement Learning
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    Chapter 7 The Evolution of Representations in Genetic Programming Trees
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    Chapter 8 How Competitive Is Genetic Programming in Business Data Science Applications?
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    Chapter 9 Using Modularity Metrics as Design Features to Guide Evolution in Genetic Programming
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    Chapter 10 Evolutionary Computation and AI Safety
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    Chapter 11 Genetic Programming Symbolic Regression: What Is the Prior on the Prediction?
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    Chapter 12 Hands-on Artificial Evolution Through Brain Programming
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    Chapter 13 Comparison of Linear Genome Representations for Software Synthesis
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    Chapter 14 Enhanced Optimization with Composite Objectives and Novelty Pulsation
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    Chapter 15 New Pathways in Coevolutionary Computation
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    Chapter 16 2019 Evolutionary Algorithms Review
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    Chapter 17 Evolving a Dota 2 Hero Bot with a Probabilistic Shared Memory Model
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    Chapter 18 Modelling Genetic Programming as a Simple Sampling Algorithm
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    Chapter 19 An Evolutionary System for Better Automatic Software Repair
Attention for Chapter 13: Comparison of Linear Genome Representations for Software Synthesis
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Chapter title
Comparison of Linear Genome Representations for Software Synthesis
Chapter number 13
Book title
Genetic Programming Theory and Practice XVII
Published by
Springer, Cham, May 2020
DOI 10.1007/978-3-030-39958-0_13
Book ISBNs
978-3-03-039957-3, 978-3-03-039958-0

Edward Pantridge, Thomas Helmuth, Lee Spector, Pantridge, Edward, Helmuth, Thomas, Spector, Lee

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 2 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 2 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 1 50%
Unknown 1 50%
Readers by discipline Count As %
Computer Science 1 50%
Unknown 1 50%