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Parallel Problem Solving from Nature – PPSN XVI

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Cover of 'Parallel Problem Solving from Nature – PPSN XVI'

Table of Contents

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    Book Overview
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    Chapter 1 Evolving Deep Forest with Automatic Feature Extraction for Image Classification Using Genetic Programming
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    Chapter 2 Fast Perturbative Algorithm Configurators
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    Chapter 3 Dominance, Indicator and Decomposition Based Search for Multi-objective QAP: Landscape Analysis and Automated Algorithm Selection
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    Chapter 4 Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem
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    Chapter 5 Automatic Configuration of a Multi-objective Local Search for Imbalanced Classification
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    Chapter 6 Multi-fidelity Optimization Approach Under Prior and Posterior Constraints and Its Application to Compliance Minimization
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    Chapter 7 Model-Based Algorithm Configuration with Default-Guided Probabilistic Sampling
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    Chapter 8 Evolving Sampling Strategies for One-Shot Optimization Tasks
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    Chapter 9 A Surrogate-Assisted Evolutionary Algorithm with Random Feature Selection for Large-Scale Expensive Problems
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    Chapter 10 Designing Air Flow with Surrogate-Assisted Phenotypic Niching
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    Chapter 11 Variance Reduction for Better Sampling in Continuous Domains
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    Chapter 12 High Dimensional Bayesian Optimization Assisted by Principal Component Analysis
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    Chapter 13 Simple Surrogate Model Assisted Optimization with Covariance Matrix Adaptation
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    Chapter 14 Proposal of a Realistic Many-Objective Test Suite
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    Chapter 15 Approximate Hypervolume Calculation with Guaranteed or Confidence Bounds
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    Chapter 16 Can Compact Optimisation Algorithms Be Structurally Biased?
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    Chapter 17 Parallelized Bayesian Optimization for Expensive Robot Controller Evolution
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    Chapter 18 Revisiting Population Models in Differential Evolution on a Limited Budget of Evaluations
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    Chapter 19 Continuous Optimization Benchmarks by Simulation
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    Chapter 20 Comparative Run-Time Performance of Evolutionary Algorithms on Multi-objective Interpolated Continuous Optimisation Problems
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    Chapter 21 On the Design of a Partition Crossover for the Quadratic Assignment Problem
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    Chapter 22 A Permutational Boltzmann Machine with Parallel Tempering for Solving Combinatorial Optimization Problems
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    Chapter 23 Solution Repair by Inequality Network Propagation in LocalSolver
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    Chapter 24 Optimising Tours for the Weighted Traveling Salesperson Problem and the Traveling Thief Problem: A Structural Comparison of Solutions
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    Chapter 25 Decentralized Combinatorial Optimization
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    Chapter 26 PbO-CCSAT : Boosting Local Search for Satisfiability Using Programming by Optimisation
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    Chapter 27 Evaluation of a Permutation-Based Evolutionary Framework for Lyndon Factorizations
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    Chapter 28 Optimising Monotone Chance-Constrained Submodular Functions Using Evolutionary Multi-objective Algorithms
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    Chapter 29 Parameter-Less Population Pyramid for Permutation-Based Problems
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    Chapter 30 Biologically Plausible Learning of Text Representation with Spiking Neural Networks
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    Chapter 31 Multi-Objective Counterfactual Explanations
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    Chapter 32 Multi-objective Magnitude-Based Pruning for Latency-Aware Deep Neural Network Compression
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    Chapter 33 Network Representation Learning Based on Topological Structure and Vertex Attributes
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    Chapter 34 A Committee of Convolutional Neural Networks for Image Classification in the Concurrent Presence of Feature and Label Noise
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    Chapter 35 Improving Imbalanced Classification by Anomaly Detection
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    Chapter 36 BACS: A Thorough Study of Using Behavioral Sequences in ACS2
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    Chapter 37 Nash Equilibrium as a Solution in Supervised Classification
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    Chapter 38 Analyzing the Components of Distributed Coevolutionary GAN Training
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    Chapter 39 Canonical Correlation Discriminative Learning for Domain Adaptation
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    Chapter 40 Improving Sampling in Evolution Strategies Through Mixture-Based Distributions Built from Past Problem Instances
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    Chapter 41 The Hessian Estimation Evolution Strategy
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    Chapter 42 Large Population Sizes and Crossover Help in Dynamic Environments
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    Chapter 43 Neuromemetic Evolutionary Optimization
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    Chapter 44 Evolved Gossip Contracts - A Framework for Designing Multi-agent Systems
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    Chapter 45 A SHADE-Based Algorithm for Large Scale Global Optimization
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    Chapter 46 Evolutionary Algorithms with Self-adjusting Asymmetric Mutation
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    Chapter 47 Behavior Optimization in Large Distributed Systems Modeled by Cellular Automata
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    Chapter 48 Learning Step-Size Adaptation in CMA-ES
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    Chapter 49 Sparse Inverse Covariance Learning for CMA-ES with Graphical Lasso
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    Chapter 50 Adaptive Stochastic Natural Gradient Method for Optimizing Functions with Low Effective Dimensionality
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Title
Parallel Problem Solving from Nature – PPSN XVI
Published by
Springer International Publishing, November 2020
DOI 10.1007/978-3-030-58112-1
ISBNs
978-3-03-058111-4, 978-3-03-058112-1
Editors

Bäck, Thomas, Preuss, Mike, Deutz, André, Wang, Hao, Doerr, Carola, Emmerich, Michael, Trautmann, Heike

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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 > Ph. D. Student 1 50%
Student > Doctoral Student 1 50%
Readers by discipline Count As %
Social Sciences 1 50%
Engineering 1 50%