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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

  1. Altmetric Badge
    Book Overview
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    Chapter 1 Generation of New Scalarizing Functions Using Genetic Programming
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    Chapter 2 The Usability Argument for Refinement Typed Genetic Programming
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    Chapter 3 Program Synthesis in a Continuous Space Using Grammars and Variational Autoencoders
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    Chapter 4 Cooperative Co-Evolutionary Genetic Programming for High Dimensional Problems
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    Chapter 5 Image Feature Learning with Genetic Programming
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    Chapter 6 Learning a Formula of Interpretability to Learn Interpretable Formulas
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    Chapter 7 On Stochastic Fitness Landscapes: Local Optimality and Fitness Landscape Analysis for Stochastic Search Operators
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    Chapter 8 Fitness Landscape Analysis of Dimensionally-Aware Genetic Programming Featuring Feynman Equations
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    Chapter 9 Global Landscape Structure and the Random MAX-SAT Phase Transition
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    Chapter 10 Exploratory Landscape Analysis is Strongly Sensitive to the Sampling Strategy
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    Chapter 11 One PLOT to Show Them All: Visualization of Efficient Sets in Multi-objective Landscapes
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    Chapter 12 On Sharing Information Between Sub-populations in MOEA/S
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    Chapter 13 Multi-objective Optimization by Uncrowded Hypervolume Gradient Ascent
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    Chapter 14 An Ensemble Indicator-Based Density Estimator for Evolutionary Multi-objective Optimization
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    Chapter 15 Ensuring Smoothly Navigable Approximation Sets by Bézier Curve Parameterizations in Evolutionary Bi-objective Optimization
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    Chapter 16 Many-Objective Test Database Generation for SQL
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    Chapter 17 A New Paradigm in Interactive Evolutionary Multiobjective Optimization
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    Chapter 18 Hypervolume Optimal $$\mu $$-Distributions on Line-Based Pareto Fronts in Three Dimensions
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    Chapter 19 Adaptive Operator Selection Based on Dynamic Thompson Sampling for MOEA/D
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    Chapter 20 A Study of Swarm Topologies and Their Influence on the Performance of Multi-Objective Particle Swarm Optimizers
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    Chapter 21 Visualising Evolution History in Multi- and Many-objective Optimisation
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    Chapter 22 Improving Many-Objective Evolutionary Algorithms by Means of Edge-Rotated Cones
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    Chapter 23 Human-Like Summaries from Heterogeneous and Time-Windowed Software Development Artefacts
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    Chapter 24 A Search for Additional Structure: The Case of Cryptographic S-boxes
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    Chapter 25 Evolutionary Multi-objective Design of SARS-CoV-2 Protease Inhibitor Candidates
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    Chapter 26 Generic Relative Relations in Hierarchical Gene Expression Data Classification
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    Chapter 27 A Variable Neighborhood Search for the Job Sequencing with One Common and Multiple Secondary Resources Problem
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    Chapter 28 Evolutionary Graph-Based V+E Optimization for Protection Against Epidemics
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    Chapter 29 Human-Derived Heuristic Enhancement of an Evolutionary Algorithm for the 2D Bin-Packing Problem
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    Chapter 30 Towards Novel Meta-heuristic Algorithms for Dynamic Capacitated Arc Routing Problems
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    Chapter 31 Robust Evolutionary Bi-objective Optimization for Prostate Cancer Treatment with High-Dose-Rate Brachytherapy
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    Chapter 32 A Hybrid Evolutionary Algorithm for Reliable Facility Location Problem
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    Chapter 33 Optimality-Based Analysis of XCSF Compaction in Discrete Reinforcement Learning
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    Chapter 34 Hybridizing the 1/5-th Success Rule with Q-Learning for Controlling the Mutation Rate of an Evolutionary Algorithm
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    Chapter 35 Fitness Landscape Features and Reward Shaping in Reinforcement Learning Policy Spaces
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    Chapter 36 ClipUp: A Simple and Powerful Optimizer for Distribution-Based Policy Evolution
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    Chapter 37 Warm-Start AlphaZero Self-play Search Enhancements
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    Chapter 38 Runtime Analysis of a Heavy-Tailed $$(1+(\lambda ,\lambda ))$$ Genetic Algorithm on Jump Functions
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    Chapter 39 First Steps Towards a Runtime Analysis When Starting with a Good Solution
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    Chapter 40 Optimal Mutation Rates for the $$(1+\lambda )$$ EA on OneMax
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    Chapter 41 Maximizing Submodular or Monotone Functions Under Partition Matroid Constraints by Multi-objective Evolutionary Algorithms
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    Chapter 42 Lower Bounds for Non-elitist Evolutionary Algorithms via Negative Multiplicative Drift
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    Chapter 43 Exponential Upper Bounds for the Runtime of Randomized Search Heuristics
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    Chapter 44 Analysis on the Efficiency of Multifactorial Evolutionary Algorithms
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    Chapter 45 Improved Fixed-Budget Results via Drift Analysis
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    Chapter 46 On Averaging the Best Samples in Evolutionary Computation
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    Chapter 47 Filter Sort Is $$\varOmega (N^3)$$ in the Worst Case
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    Chapter 48 Approximation Speed-Up by Quadratization on LeadingOnes
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    Chapter 49 Benchmarking a $$(\mu +\lambda )$$ Genetic Algorithm with Configurable Crossover Probability
Attention for Chapter 2: The Usability Argument for Refinement Typed Genetic Programming
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Chapter title
The Usability Argument for Refinement Typed Genetic Programming
Chapter number 2
Book title
Parallel Problem Solving from Nature – PPSN XVI
Published by
Springer, Cham, September 2020
DOI 10.1007/978-3-030-58115-2_2
Book ISBNs
978-3-03-058114-5, 978-3-03-058115-2
Authors

Alcides Fonseca, Paulo Santos, Sara Silva

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Mendeley readers

The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 33%
Researcher 1 11%
Student > Postgraduate 1 11%
Other 1 11%
Unknown 3 33%
Readers by discipline Count As %
Computer Science 4 44%
Agricultural and Biological Sciences 1 11%
Engineering 1 11%
Unknown 3 33%