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Parallel Problem Solving from Nature – PPSN X

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

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 On the Behaviour of the (1+1)-ES for a Simple Constrained Problem
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    Chapter 2 σ-Self-Adaptive Weighted Multirecombination Evolution Strategy with Scaled Weights on the Noisy Sphere
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    Chapter 3 Convergence Analysis of Evolution Strategies with Random Numbers of Offspring
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    Chapter 4 Parallel Problem Solving from Nature – PPSN X
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    Chapter 5 A Blend of Markov-Chain and Drift Analysis
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    Chapter 6 On Multiplicative Noise Models for Stochastic Search
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    Chapter 7 Premature Convergence in Constrained Continuous Search Spaces
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    Chapter 8 Approximating Minimum Multicuts by Evolutionary Multi-objective Algorithms
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    Chapter 9 Simplified Drift Analysis for Proving Lower Bounds in Evolutionary Computation
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    Chapter 10 Ignoble Trails - Where Crossover Is Provably Harmful
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    Chapter 11 Lower Bounds for Evolution Strategies Using VC-Dimension
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    Chapter 12 Rigorous Runtime Analysis of Inversely Fitness Proportional Mutation Rates
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    Chapter 13 Covariance Matrix Adaptation Revisited – The CMSA Evolution Strategy –
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    Chapter 14 Enhancing the Performance of Maximum–Likelihood Gaussian EDAs Using Anticipated Mean Shift
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    Chapter 15 New Approaches to Coevolutionary Worst-Case Optimization
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    Chapter 16 Bio-inspired Search and Distributed Memory Formation on Power-Law Networks
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    Chapter 17 Enhancing the Efficiency of the ECGA
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    Chapter 18 Extreme Value Based Adaptive Operator Selection
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    Chapter 19 Uncertainty Handling in Model Selection for Support Vector Machines
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    Chapter 20 Niche Radius Adaptation with Asymmetric Sharing
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    Chapter 21 Adaptive Encoding: How to Render Search Coordinate System Invariant
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    Chapter 22 Supervised and Evolutionary Learning of Echo State Networks
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    Chapter 23 Dynamic Cooperative Coevolutionary Sensor Deployment Via Localized Fitness Evaluation
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    Chapter 24 On the Run-Time Dynamics of a Peer-to-Peer Evolutionary Algorithm
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    Chapter 25 Mixed-Integer Evolution Strategies with Dynamic Niching
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    Chapter 26 A Compass to Guide Genetic Algorithms
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    Chapter 27 Testing the Intermediate Disturbance Hypothesis: Effect of Asynchronous Population Incorporation on Multi-Deme Evolutionary Algorithms
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    Chapter 28 A Developmental Approach to the Uncapacitated Examination Timetabling Problem
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    Chapter 29 QFCS: A Fuzzy LCS in Continuous Multi-step Environments with Continuous Vector Actions
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    Chapter 30 A Simple Modification in CMA-ES Achieving Linear Time and Space Complexity
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    Chapter 31 Evolutionary Algorithms for Dynamic Environments: Prediction Using Linear Regression and Markov Chains
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    Chapter 32 Combination of Natural and Numerical Optimization Methods at the Example of an Internal Gas Turbine Cooling Channel
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    Chapter 33 When Does Quasi-random Work?
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    Chapter 34 Fitness Expectation Maximization
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    Chapter 35 Formally Testing Liveness by Means of Compression Rates
  37. Altmetric Badge
    Chapter 36 Parallel Problem Solving from Nature – PPSN X
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    Chapter 37 Sub-tree Swapping Crossover, Allele Diffusion and GP Convergence
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    Chapter 38 How Single Ant ACO Systems Optimize Pseudo-Boolean Functions
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    Chapter 39 Actuation Constraints and Artificial Physics Control
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    Chapter 40 Genetic Repair for Optimization under Constraints Inspired by Arabidopsis Thaliana
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    Chapter 41 Improved Multilabel Classification with Neural Networks
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    Chapter 42 Enhancing Efficiency of Hierarchical BOA Via Distance-Based Model Restrictions
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    Chapter 43 Evolution Strategies for Direct Policy Search
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    Chapter 44 Optimal Nesting of Species for Exact Cover: Many against Many
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    Chapter 45 Nonsynonymous to Synonymous Substitution Ratio \(k_{\mathrm a}/k_{\mathrm s}\) : Measurement for Rate of Evolution in Evolutionary Computation
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    Chapter 46 Examining the Effect of Elitism in Cellular Genetic Algorithms Using Two Neighborhood Structures
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    Chapter 47 Parallel Problem Solving from Nature – PPSN X
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    Chapter 48 Reinforcement Learning: Insights from Interesting Failures in Parameter Selection
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    Chapter 49 Evolvable Agents in Static and Dynamic Optimization Problems
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    Chapter 50 The Impact of Global Structure on Search
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    Chapter 51 Improved Lower Limits for Pheromone Trails in Ant Colony Optimization
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    Chapter 52 Evolving Neural Networks for Online Reinforcement Learning
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    Chapter 53 Costs and Benefits of Tuning Parameters of Evolutionary Algorithms
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    Chapter 54 Cooperation in Co-evolving Networks: The Prisoner’s Dilemma and Stag-Hunt Games
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    Chapter 55 Preventing Premature Convergence in a Simple EDA Via Global Step Size Setting
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    Chapter 56 A Steady-State Genetic Algorithm with Resampling for Noisy Inventory Control
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    Chapter 57 EA-Powered Basin Number Estimation by Means of Preservation and Exploration
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    Chapter 58 Coevolving Cellular Automata with Memory for Chemical Computing: Boolean Logic Gates in the B-Z Reaction
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    Chapter 59 The Influence of Mutation on Protein-Ligand Docking Optimization: A Locality Analysis
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    Chapter 60 Testing the CAX on a Real-World Problem and Other Benchmarks
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    Chapter 61 Countering Poisonous Inputs with Memetic Neuroevolution
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    Chapter 62 Parameter Control Methods for Selection Operators in Genetic Algorithms
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    Chapter 63 Evaluation and Diversity in Co-evolution
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    Chapter 64 Comparison of Adaptive Approaches for Differential Evolution
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    Chapter 65 Analyzing Hypervolume Indicator Based Algorithms
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    Chapter 66 Solving Three-Objective Optimization Problems Using a New Hybrid Cellular Genetic Algorithm
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    Chapter 67 Runtime Analyses for Using Fairness in Evolutionary Multi-Objective Optimization
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    Chapter 68 The Parallel Predator-Prey Model: A Step towards Practical Application
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    Chapter 69 Functional-Specialization Multi-Objective Real-Coded Genetic Algorithm: FS-MOGA
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    Chapter 70 Parallel Problem Solving from Nature – PPSN X
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    Chapter 71 On the Use of Projected Gradients for Constrained Multiobjective Optimization Problems
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    Chapter 72 Diversity Maintenance Mechanism for Multi-Objective Genetic Algorithms Using Clustering and Network Inversion
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    Chapter 73 Many Objective Optimisation: Direct Objective Boundary Identification
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    Chapter 74 Use of Heuristic Local Search for Single-Objective Optimization in Multiobjective Memetic Algorithms
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    Chapter 75 Distance Based Ranking in Many-Objective Particle Swarm Optimization
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    Chapter 76 A Study of Convergence Speed in Multi-objective Metaheuristics
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    Chapter 77 Team Algorithms Based on Ant Colony Optimization – A New Multi-Objective Optimization Approach
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    Chapter 78 Multiobjective Optimization on a Limited Budget of Evaluations Using Model-Assisted \(\mathcal{S}\) -Metric Selection
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    Chapter 79 Approximating the Knee of an MOP with Stochastic Search Algorithms
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    Chapter 80 Approximate Solutions in Space Mission Design
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    Chapter 81 A Local Search Based Evolutionary Multi-objective Optimization Approach for Fast and Accurate Convergence
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    Chapter 82 A Convergence Criterion for Multiobjective Evolutionary Algorithms Based on Systematic Statistical Testing
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    Chapter 83 A Proposal to Hybridize Multi-Objective Evolutionary Algorithms with Non-gradient Mathematical Programming Techniques
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    Chapter 84 SPAM: Set Preference Algorithm for Multiobjective Optimization
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    Chapter 85 Modeling Human Expertise on a Cheese Ripening Industrial Process Using GP
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    Chapter 86 Readable and Accurate Rulesets with ORGA
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    Chapter 87 A Distributed Memetic Algorithm for the Routing and Wavelength Assignment Problem
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    Chapter 88 Theoretical Analysis of Initial Particle Swarm Behavior
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    Chapter 89 Large-Scale Optimization of Non-separable Building-Block Problems
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    Chapter 90 Particle Filter with Swarm Move for Optimization
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    Chapter 91 A Feasibility-Preserving Crossover and Mutation Operator for Constrained Combinatorial Problems
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    Chapter 92 An Iterated Local Search Approach for Finding Provably Good Solutions for Very Large TSP Instances
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    Chapter 93 Intrinsic System Model of the Genetic Algorithm with α-Selection
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    Chapter 94 Imitation Learning in Uncertain Environments
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    Chapter 95 Using Ants’ Task Division for Better Game Engines – A Contribution to Game Accessibility for Impaired Players
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    Chapter 96 A Set-Based Particle Swarm Optimization Method
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    Chapter 97 Nature–Inspired Synthesis of Rational Protocols
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    Chapter 98 Optimizing Real-Time Ordered-Data Broadcasts in Pervasive Environments Using Evolution Strategy
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    Chapter 99 A Multiobjective Evolutionary Algorithm for the Linear Shelf Space Allocation Problem
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    Chapter 100 A Memetic Algorithm for the Delineation of Local Labour Markets
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    Chapter 101 Evolving XSLT Stylesheets for Document Transformation
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    Chapter 102 Fast Multi-objective Scheduling of Jobs to Constrained Resources Using a Hybrid Evolutionary Algorithm
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    Chapter 103 Virus Evolution Strategy for Vehicle Routing Problems with Time Windows
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    Chapter 104 Learning Fuzzy Rules with Evolutionary Algorithms — An Analytic Approach
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    Chapter 105 Evolving Regular Expressions for GeneChip Probe Performance Prediction
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    Chapter 106 Evolutionary Market Agents for Resource Allocation in Decentralised Systems
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    Chapter 107 GA-Net: A Genetic Algorithm for Community Detection in Social Networks
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    Chapter 108 Learning Walking Patterns for Kinematically Complex Robots Using Evolution Strategies
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    Chapter 109 Driving Cars by Means of Genetic Algorithms
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    Chapter 110 AGE-P: A Platform for Open Evolution
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    Chapter 111 Adding Probabilistic Dependencies to the Search of Protein Side Chain Configurations Using EDAs
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    Chapter 112 A Scalable Formal Framework for Analyzing the Behavior of Nature-Inspired Routing Protocols
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    Chapter 113 A Grouping Genetic Algorithm Using Linear Linkage Encoding for Bin Packing
  115. Altmetric Badge
    Chapter 114 Optimization of Feature Processing Chain in Music Classification by Evolution Strategies
Attention for Chapter 36: Parallel Problem Solving from Nature – PPSN X
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (64th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (58th percentile)

Mentioned by

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1 Wikipedia page

Citations

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24 Mendeley
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Chapter title
Parallel Problem Solving from Nature – PPSN X
Chapter number 36
Book title
Parallel Problem Solving from Nature – PPSN X
Published in
Lecture notes in computer science, September 2008
DOI 10.1007/978-3-540-87700-4_36
Book ISBNs
978-3-54-087699-1, 978-3-54-087700-4
Authors

Jeff Clune, Charles Ofria, Robert T. Pennock

Editors

Günter Rudolph, Thomas Jansen, Nicola Beume, Simon Lucas, Carlo Poloni

Mendeley readers

The data shown below were compiled from readership statistics for 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 2 8%
Norway 1 4%
France 1 4%
Brazil 1 4%
Canada 1 4%
Unknown 18 75%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 42%
Student > Master 4 17%
Other 2 8%
Professor > Associate Professor 2 8%
Researcher 2 8%
Other 2 8%
Unknown 2 8%
Readers by discipline Count As %
Computer Science 18 75%
Agricultural and Biological Sciences 2 8%
Engineering 1 4%
Unknown 3 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 13 December 2016.
All research outputs
#2,301,002
of 8,763,848 outputs
Outputs from Lecture notes in computer science
#1,698
of 6,771 outputs
Outputs of similar age
#101,145
of 299,636 outputs
Outputs of similar age from Lecture notes in computer science
#62
of 163 outputs
Altmetric has tracked 8,763,848 research outputs across all sources so far. This one has received more attention than most of these and is in the 60th percentile.
So far Altmetric has tracked 6,771 research outputs from this source. They receive a mean Attention Score of 4.0. This one has gotten more attention than average, scoring higher than 62% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 299,636 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 64% of its contemporaries.
We're also able to compare this research output to 163 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 58% of its contemporaries.