Parallel Problem Solving from Nature, PPSN XI

Table of Contents

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   Book Overview
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   Chapter 1 Optimal Fixed and Adaptive Mutation Rates for the LeadingOnes Problem
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   Chapter 2 Mirrored Sampling and Sequential Selection for Evolution Strategies
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   Chapter 3 Optimisation and Generalisation: Footprints in Instance Space
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   Chapter 4 Adaptive Drift Analysis
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   Chapter 5 Optimizing Monotone Functions Can Be Difficult
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   Chapter 6 Log-Linear Convergence of the Scale-Invariant (μ/μ w ,λ)-ES and Optimal μ for Intermediate Recombination for Large Population Sizes
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   Chapter 7 Exploiting Overlap When Searching for Robust Optima
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   Chapter 8 Benchmarking Evolutionary Algorithms: Towards Exploratory Landscape Analysis
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    Chapter 9 Parallel Problem Solving from Nature, PPSN XI
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    Chapter 10 When Does Dependency Modelling Help? Using a Randomized Landscape Generator to Compare Algorithms in Terms of Problem Structure
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    Chapter 11 Parallel Problem Solving from Nature, PPSN XI
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    Chapter 12 Differential Mutation Based on Population Covariance Matrix
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    Chapter 13 General Lower Bounds for the Running Time of Evolutionary Algorithms
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    Chapter 14 A Binary Encoding Supporting Both Mutation and Recombination
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    Chapter 15 Towards Analyzing Recombination Operators in Evolutionary Search
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    Chapter 16 Bidirectional Relation between CMA Evolution Strategies and Natural Evolution Strategies
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    Chapter 17 A Fine-Grained View of GP Locality with Binary Decision Diagrams as Ant Phenotypes
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    Chapter 18 Drift Analysis with Tail Bounds
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    Chapter 19 More Effective Crossover Operators for the All-Pairs Shortest Path Problem
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    Chapter 20 Comparison-Based Adaptive Strategy Selection with Bandits in Differential Evolution
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    Chapter 21 Fixed Parameter Evolutionary Algorithms and Maximum Leaf Spanning Trees: A Matter of Mutation
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    Chapter 22 An Archive Maintenance Scheme for Finding Robust Solutions
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    Chapter 23 Experimental Supplements to the Theoretical Analysis of Migration in the Island Model
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    Chapter 24 General Scheme for Analyzing Running Times of Parallel Evolutionary Algorithms
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    Chapter 25 Negative Drift in Populations
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    Chapter 26 Log(λ) Modifications for Optimal Parallelism
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    Chapter 27 The Linkage Tree Genetic Algorithm
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    Chapter 28 An Analysis of the XOR Dynamic Problem Generator Based on the Dynamical System
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    Chapter 29 The Role of Degenerate Robustness in the Evolvability of Multi-agent Systems in Dynamic Environments
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    Chapter 30 Evolutionary Learning of Technical Trading Rules without Data-Mining Bias
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    Chapter 31 Using Computational Intelligence to Identify Performance Bottlenecks in a Computer System
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    Chapter 32 Selecting Small Audio Feature Sets in Music Classification by Means of Asymmetric Mutation
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    Chapter 33 Globally Induced Model Trees: An Evolutionary Approach
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    Chapter 34 Open-Ended Evolutionary Robotics: An Information Theoretic Approach
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    Chapter 35 A Novel Similarity-Based Crossover for Artificial Neural Network Evolution
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    Chapter 36 Indirect Encoding of Neural Networks for Scalable Go
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    Chapter 37 Comparison-Based Optimizers Need Comparison-Based Surrogates
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    Chapter 38 A Cooperative Coevolutionary Approach to Partitional Clustering
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    Chapter 39 Feature Selection for Multi-purpose Predictive Models: A Many-Objective Task
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    Chapter 40 Incorporating Domain Knowledge into Evolutionary Computing for Discovering Gene-Gene Interaction.
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    Chapter 41 The Application of Pittsburgh-Style Learning Classifier Systems to Address Genetic Heterogeneity and Epistasis in Association Studies
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    Chapter 42 Threshold Selection, Mitosis and Dual Mutation in Cooperative Co-evolution: Application to Medical 3D Tomography
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    Chapter 43 Comparative Analysis of Search and Score Metaheuristics for Bayesian Network Structure Learning Using Node Juxtaposition Distributions
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    Chapter 44 Analyzing the Credit Default Swap Market Using Cartesian Genetic Programming
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    Chapter 45 A Memetic Cooperative Optimization Schema and Its Application to the Tool Switching Problem
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    Chapter 46 Ownership and Trade in Spatial Evolutionary Memetic Games
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    Chapter 47 A Hyper-Heuristic Approach to Strip Packing Problems
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    Chapter 48 Asymptotic Analysis of Computational Multi-Agent Systems
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    Chapter 49 Path-Guided Mutation for Stochastic Pareto Local Search Algorithms
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    Chapter 50 Scheduling English Football Fixtures over the Holiday Period Using Hyper-heuristics
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    Chapter 51 Graph Clustering Based Model Building
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    Chapter 52 How to Choose Solutions for Local Search in Multiobjective Combinatorial Memetic Algorithms
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    Chapter 53 Secure and Task Abortion Aware GA-Based Hybrid Metaheuristics for Grid Scheduling
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    Chapter 54 A Memetic Algorithm for the Pickup and Delivery Problem with Time Windows Using Selective Route Exchange Crossover
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    Chapter 55 Ant Based Hyper Heuristics with Space Reduction: A Case Study of the p-Median Problem
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    Chapter 56 A Study of Multi-parent Crossover Operators in a Memetic Algorithm
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    Chapter 57 A Hybrid Genetic Algorithm for the Traveling Salesman Problem Using Generalized Partition Crossover
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    Chapter 58 A Memetic Algorithm with Non Gradient-Based Local Search Assisted by a Meta-model
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    Chapter 59 Theoretically Investigating Optimal μ-Distributions for the Hypervolume Indicator: First Results for Three Objectives
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    Chapter 60 Convergence Rates of (1+1) Evolutionary Multiobjective Optimization Algorithms
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    Chapter 61 Tight Bounds for the Approximation Ratio of the Hypervolume Indicator
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    Chapter 62 Evolutionary Multiobjective Optimization Algorithm as a Markov System
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    Chapter 63 A Natural Evolution Strategy for Multi-objective Optimization
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    Chapter 64 Solving Multiobjective Optimization Problem by Constraint Optimization
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    Chapter 65 Enhancing Diversity for Average Ranking Method in Evolutionary Many-Objective Optimization
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    Chapter 66 Objective Space Partitioning Using Conflict Information for Many-Objective Optimization
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    Chapter 67 How Crossover Speeds Up Evolutionary Algorithms for the Multi-criteria All-Pairs-Shortest-Path Problem
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    Chapter 68 Path Relinking on Many-Objective NK-Landscapes
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    Chapter 69 In Search of Equitable Solutions Using Multi-objective Evolutionary Algorithms
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    Chapter 70 Stopping Criteria for Genetic Algorithms with Application to Multiobjective Optimization
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    Chapter 71 Defining and Optimizing Indicator-Based Diversity Measures in Multiobjective Search
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    Chapter 72 On Expected-Improvement Criteria for Model-based Multi-objective Optimization
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    Chapter 73 Parameter Tuning Boosts Performance of Variation Operators in Multiobjective Optimization