Applications of Evolutionary Computation

Table of Contents

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   Book Overview
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   Chapter 1 Hindsight Experience Replay with Evolutionary Decision Trees for Curriculum Goal Generation
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   Chapter 2 Cultivating Diversity: A Comparison of Diversity Objectives in Neuroevolution
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   Chapter 3 Evolving Reservoirs for Meta Reinforcement Learning
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   Chapter 4 Hybrid Surrogate Assisted Evolutionary Multiobjective Reinforcement Learning for Continuous Robot Control
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   Chapter 5 Towards Physical Plausibility in Neuroevolution Systems
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   Chapter 6 Leveraging More of Biology in Evolutionary Reinforcement Learning
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   Chapter 7 A Hierarchical Dissimilarity Metric for Automated Machine Learning Pipelines, and Visualizing Search Behaviour
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   Chapter 8 DeepEMO: A Multi-indicator Convolutional Neural Network-Based Evolutionary Multi-objective Algorithm
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    Chapter 9 A Comparative Analysis of Evolutionary Adversarial One-Pixel Attacks
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    Chapter 10 Robust Neural Architecture Search Using Differential Evolution for Medical Images
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    Chapter 11 Progressive Self-supervised Multi-objective NAS for Image Classification
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    Chapter 12 Genetic Programming with Aggregate Channel Features for Flower Localization Using Limited Training Data
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    Chapter 13 Evolutionary Multi-objective Optimization of Large Language Model Prompts for Balancing Sentiments
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    Chapter 14 Evolutionary Feature-Binning with Adaptive Burden Thresholding for Biomedical Risk Stratification
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    Chapter 15 An Evolutionary Deep Learning Approach for Efficient Quantum Algorithms Transpilation
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    Chapter 16 Measuring Similarities in Model Structure of Metaheuristic Rule Set Learners
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    Chapter 17 Incremental Growth on Compositional Pattern Producing Networks Based Optimization of Biohybrid Actuators
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    Chapter 18 Hilbert Curves for Efficient Exploratory Landscape Analysis Neighbourhood Sampling
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    Chapter 19 Predicting Algorithm Performance in Constrained Multiobjective Optimization: A Tough Nut to Crack
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    Chapter 20 On the Latent Structure of the bbob-biobj Test Suite
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    Chapter 21 Strategies for Evolving Diverse and Effective Behaviours in Pursuit Domains
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    Chapter 22 Using Evolution and Deep Learning to Generate Diverse Intelligent Agents
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    Chapter 23 Vision Transformers for Computer Go
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    Chapter 24 Integrating Bayesian and Evolutionary Approaches for Multi-objective Optimisation