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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 11: Genetic Programming Symbolic Regression: What Is the Prior on the Prediction?
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