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Parallel Problem Solving from Nature – PPSN XIV

Overview of attention for book
Parallel Problem Solving from Nature – PPSN XIV
Springer International Publishing

Table of Contents

  1. Altmetric Badge
    Book Overview
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    Chapter 1 Online Model Selection for Restricted Covariance Matrix Adaptation
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    Chapter 2 Genotype Regulation by Self-modifying Instruction-Based Development on Cellular Automata
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    Chapter 3 Evolution Under Strong Noise: A Self-Adaptive Evolution Strategy Can Reach the Lower Performance Bound - The pcCMSA-ES
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    Chapter 4 An Evolutionary Hyper-heuristic for the Software Project Scheduling Problem
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    Chapter 5 The Multiple Insertion Pyramid: A Fast Parameter-Less Population Scheme
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    Chapter 6 Doubly Trained Evolution Control for the Surrogate CMA-ES
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    Chapter 7 Efficient Global Optimization with Indefinite Kernels
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    Chapter 8 A Fitness Cloud Model for Adaptive Metaheuristic Selection Methods
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    Chapter 9 A Study of the Performance of \(\text {Self-}{\star }\)  Memetic Algorithms on Heterogeneous Ephemeral Environments
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    Chapter 10 Lyapunov Design of a Simple Step-Size Adaptation Strategy Based on Success
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    Chapter 11 TADE: Tight Adaptive Differential Evolution
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    Chapter 12 An Extension of Algebraic Differential Evolution for the Linear Ordering Problem with Cumulative Costs
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    Chapter 13 Analysing the Performance of Migrating Birds Optimisation Approaches for Large Scale Continuous Problems
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    Chapter 14 How Far Are We from an Optimal, Adaptive DE?
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    Chapter 15 Feature Based Algorithm Configuration: A Case Study with Differential Evolution
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    Chapter 16 An Asynchronous and Steady State Update Strategy for the Particle Swarm Optimization Algorithm
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    Chapter 17 Augmented Lagrangian Constraint Handling for CMA-ES — Case of a Single Linear Constraint
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    Chapter 18 An Active-Set Evolution Strategy for Optimization with Known Constraints
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    Chapter 19 Speciated Evolutionary Algorithm for Dynamic Constrained Optimisation
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    Chapter 20 On Constraint Handling in Surrogate-Assisted Evolutionary Many-Objective Optimization
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    Chapter 21 Artificially Inducing Environmental Changes in Evolutionary Dynamic Optimization
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    Chapter 22 Efficient Sampling When Searching for Robust Solutions
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    Chapter 23 Optimising Quantisation Noise in Energy Measurement
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    Chapter 24 Syntactical Similarity Learning by Means of Grammatical Evolution
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    Chapter 25 Hierarchical Knowledge in Self-Improving Grammar-Based Genetic Programming
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    Chapter 26 Parallel Problem Solving from Nature – PPSN XIV
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    Chapter 27 On the Non-uniform Redundancy in Grammatical Evolution
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    Chapter 28 Tournament Selection Based on Statistical Test in Genetic Programming
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    Chapter 29 Kin Selection with Twin Genetic Programming
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    Chapter 30 Using Scaffolding with Partial Call-Trees to Improve Search
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    Chapter 31 Feature Extraction for Surrogate Models in Genetic Programming
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    Chapter 32 A General-Purpose Framework for Genetic Improvement
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    Chapter 33 On the Use of Semantics in Multi-objective Genetic Programming
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    Chapter 34 Semantic Forward Propagation for Symbolic Regression
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    Chapter 35 Reducing Dimensionality to Improve Search in Semantic Genetic Programming
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    Chapter 36 iMOACO \(_\mathbb {R}\) : A New Indicator-Based Multi-objective Ant Colony Optimization Algorithm for Continuous Search Spaces
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    Chapter 37 Variable Interaction in Multi-objective Optimization Problems
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    Chapter 38 Improving Efficiency of Bi-level Worst Case Optimization
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    Chapter 39 Multi-objective Selection of Algorithm Portfolios: Experimental Validation
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    Chapter 40 Multi-objective Local Search Based on Decomposition
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    Chapter 41 Analyzing Inter-objective Relationships: A Case Study of Software Upgradability
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    Chapter 42 Multicriteria Building Spatial Design with Mixed Integer Evolutionary Algorithms
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    Chapter 43 The Competing Travelling Salespersons Problem Under Multi-criteria
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    Chapter 44 A Parallel Multi-objective Memetic Algorithm Based on the IGD+ Indicator
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    Chapter 45 Towards Automatic Testing of Reference Point Based Interactive Methods
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    Chapter 46 Towards Many-Objective Optimisation with Hyper-heuristics: Identifying Good Heuristics with Indicators
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    Chapter 47 Use of Piecewise Linear and Nonlinear Scalarizing Functions in MOEA/D
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    Chapter 48 Pareto Inspired Multi-objective Rule Fitness for Noise-Adaptive Rule-Based Machine Learning
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    Chapter 49 Decomposition-Based Approach for Solving Large Scale Multi-objective Problems
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    Chapter 50 An Evolutionary Framework for Replicating Neurophysiological Data with Spiking Neural Networks
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    Chapter 51 A Cross-Platform Assessment of Energy Consumption in Evolutionary Algorithms
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    Chapter 52 Comparing Asynchronous and Synchronous Parallelization of the SMS-EMOA
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    Chapter 53 A Parallel Version of SMS-EMOA for Many-Objective Optimization Problems
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    Chapter 54 Evolution of Active Categorical Image Classification via Saccadic Eye Movement
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    Chapter 55 Cooperative Coevolution of Control for a Real Multirobot System
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    Chapter 56 Replicating the Stroop Effect Using a Developmental Spatial Neuroevolution System
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    Chapter 57 Evolving Cryptographic Pseudorandom Number Generators
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    Chapter 58 Exploring Uncertainty and Movement in Categorical Perception Using Robots
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    Chapter 59 Community Structure Detection for the Functional Connectivity Networks of the Brain
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    Chapter 60 Data Classification Using Carbon-Nanotubes and Evolutionary Algorithms
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    Chapter 61 WS Network Design Problem with Nonlinear Pricing Solved by Hybrid Algorithm
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    Chapter 62 A Novel Efficient Mutation for Evolutionary Design of Combinational Logic Circuits
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    Chapter 63 Fast and Effective Multi-objective Optimisation of Submerged Wave Energy Converters
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    Chapter 64 Evolution of Spiking Neural Networks Robust to Noise and Damage for Control of Simple Animats
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    Chapter 65 Anomaly Detection with the Voronoi Diagram Evolutionary Algorithm
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    Chapter 66 Evolving Spatially Aggregated Features from Satellite Imagery for Regional Modeling
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    Chapter 67 A Hybrid Autoencoder and Density Estimation Model for Anomaly Detection
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    Chapter 68 Parameterized Analysis of Multi-objective Evolutionary Algorithms and the Weighted Vertex Cover Problem
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    Chapter 69 Fixed-Parameter Single Objective Search Heuristics for Minimum Vertex Cover
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    Chapter 70 What Does the Evolution Path Learn in CMA-ES?
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    Chapter 71 Graceful Scaling on Uniform Versus Steep-Tailed Noise
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    Chapter 72 On the Robustness of Evolving Populations
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    Chapter 73 Provably Optimal Self-adjusting Step Sizes for Multi-valued Decision Variables
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    Chapter 74 Example Landscapes to Support Analysis of Multimodal Optimisation
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    Chapter 75 Self-adaptation of Mutation Rates in Non-elitist Populations
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    Chapter 76 Hypervolume Sharpe-Ratio Indicator: Formalization and First Theoretical Results
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    Chapter 77 k-Bit Mutation with Self-Adjusting k Outperforms Standard Bit Mutation
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    Chapter 78 Selection Hyper-heuristics Can Provably Be Helpful in Evolutionary Multi-objective Optimization
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    Chapter 79 RK-EDA: A Novel Random Key Based Estimation of Distribution Algorithm
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    Chapter 80 REMEDA: Random Embedding EDA for Optimising Functions with Intrinsic Dimension
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    Chapter 81 Feature-Based Diversity Optimization for Problem Instance Classification
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    Chapter 82 Searching for Quality Diversity When Diversity is Unaligned with Quality
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    Chapter 83 Emergence of Diversity and Its Benefits for Crossover in Genetic Algorithms
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    Chapter 84 Coarse-Grained Barrier Trees of Fitness Landscapes
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    Chapter 85 Rapid Phenotypic Landscape Exploration Through Hierarchical Spatial Partitioning
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    Chapter 86 Understanding Environmental Influence in an Open-Ended Evolutionary Algorithm
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    Chapter 87 Simple Random Sampling Estimation of the Number of Local Optima
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    Chapter 88 evoVision3D: A Multiscale Visualization of Evolutionary Histories
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    Chapter 89 Landscape Features for Computationally Expensive Evaluation Functions: Revisiting the Problem of Noise
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    Chapter 90 Towards Analyzing Multimodality of Continuous Multiobjective Landscapes
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    Chapter 91 Population Diversity Measures Based on Variable-Order Markov Models for the Traveling Salesman Problem
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    Chapter 92 Convergence Versus Diversity in Multiobjective Optimization
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    Chapter 93 Tunnelling Crossover Networks for the Asymmetric TSP
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    Chapter 94 The Workshops at PPSN 2016
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    Chapter 95 Tutorials at PPSN 2016
Attention for Chapter 88: evoVision3D: A Multiscale Visualization of Evolutionary Histories
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Chapter title
evoVision3D: A Multiscale Visualization of Evolutionary Histories
Chapter number 88
Book title
Parallel Problem Solving from Nature – PPSN XIV
Published in
Lecture notes in computer science, August 2016
DOI 10.1007/978-3-319-45823-6_88
Book ISBNs
978-3-31-945822-9, 978-3-31-945823-6
Authors

Justin J. Kelly, Christian Jacob

Editors

Julia Handl, Emma Hart, Peter R. Lewis, Manuel López-Ibáñez, Gabriela Ochoa, Ben Paechter

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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%
Student > Master 1 50%
Readers by discipline Count As %
Computer Science 2 100%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 20 September 2016.
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#15,314,171
of 22,776,824 outputs
Outputs from Lecture notes in computer science
#4,646
of 8,124 outputs
Outputs of similar age
#215,146
of 337,212 outputs
Outputs of similar age from Lecture notes in computer science
#264
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So far Altmetric has tracked 8,124 research outputs from this source. They receive a mean Attention Score of 5.0. This one is in the 27th percentile – i.e., 27% of its peers scored the same or lower than it.
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We're also able to compare this research output to 485 others from the same source and published within six weeks on either side of this one. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.