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Parallel Problem Solving from Nature — PPSN V : 5th International Conference Amsterdam, The Netherlands September 27–30, 1998 Proceedings

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Cover of 'Parallel Problem Solving from Nature — PPSN V : 5th International Conference Amsterdam, The Netherlands September 27–30, 1998 Proceedings'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 Modelling genetic algorithms: From Markov chains to dependence with complete connections
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    Chapter 2 On the optimization of unimodal functions with the (1+1) evolutionary algorithm
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    Chapter 3 A timing analysis of convergence to fitness sharing equilibrium
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    Chapter 4 Where elitists start limping evolution strategies at ridge functions
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    Chapter 5 A bit-wise epistasis measure for binary search spaces
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    Chapter 6 Inside GA dynamics: Ground basis for comparison
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    Chapter 7 The effect of spin-flip symmetry on the performance of the simple GA
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    Chapter 8 Fitness distance correlation and Ridge functions
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    Chapter 9 Accelerating the convergence of evolutionary algorithms by fitness landscape approximation
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    Chapter 10 Modeling building-block interdependency
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    Chapter 11 Mutate large, but inherit small! On the analysis of rescaled mutations in ( $$\tilde 1,\tilde \lambda$$ )-ES with noisy fitness data
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    Chapter 12 Creating robust solutions by means of evolutionary algorithms
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    Chapter 13 Analytic curve detection from a noisy binary edge map using genetic algorithm
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    Chapter 14 A comparison of dominance mechanisms and simple mutation on non-stationary problems
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    Chapter 15 Adaptation to a changing environment by means of the feedback thermodynamical genetic algorithm
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    Chapter 16 Optimization with noisy function evaluations
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    Chapter 17 On risky methods for local selection under noise
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    Chapter 18 Polygenic inheritance — A haploid scheme that can outperform diploidy
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    Chapter 19 Averaging efficiently in the presence of noise
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    Chapter 20 Solving binary constraint satisfaction problems using evolutionary algorithms with an adaptive fitness function
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    Chapter 21 Varying fitness functions in genetic algorithms: Studying the rate of increase of the dynamic penalty terms
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    Chapter 22 Landscape changes and the performance of Mapping Based Constraint handling methods
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    Chapter 23 A decoder-based evolutionary algorithm for constrained parameter optimization problems
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    Chapter 24 A spatial predator-prey approach to multi-objective optimization: A preliminary study
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    Chapter 25 Selective breeding in a multiobjective genetic algorithm
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    Chapter 26 Niching and elitist models for MOGAs
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    Chapter 27 Parallel evolutionary optimisation with constraint propagation
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    Chapter 28 Methods to evolve legal phenotypes
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    Chapter 29 Multiobjective optimization using evolutionary algorithms — A comparative case study
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    Chapter 30 Utilizing dynastically optimal forma recombination in hybrid genetic algorithms
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    Chapter 31 Further experimentations on the scalability of the GEMGA
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    Chapter 32 Indexed memory as a generic protocol for handling vectors of data in genetic programming
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    Chapter 33 On genetic algorithms and lindenmayer systems
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    Chapter 34 Genome length as an evolutionary self-adaptation
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    Chapter 35 Restart scheduling for genetic algorithms
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    Chapter 36 A comparative study of global and local selection in evolution strategies
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    Chapter 37 UEGO, an abstract niching technique for global optimization
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    Chapter 38 Development of problem-specific evolutionary algorithms
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    Chapter 39 The effects of control parameters and restarts on search stagnation in evolutionary programming
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    Chapter 40 Accelerating the evolutionary-gradient-search procedure: Individual step sizes
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    Chapter 41 Extending population-based incremental learning to continuous search spaces
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    Chapter 42 Multi-parent recombination in genetic algorithms with search space boundary extension by mirroring
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    Chapter 43 Selective crossover in genetic algorithms: An empirical study
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    Chapter 44 Line-breeding schemes for combinatorial optimization
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    Chapter 45 Finding regions of uncertainty in learned models: An application to face detection
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    Chapter 46 On ZCS in multi-agent environments
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    Chapter 47 Empirical analysis of the factors that affect the Baldwin effect
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    Chapter 48 Promoting generalisation of learned behaviours in genetic programming
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    Chapter 49 Generalization in Wilson's classifier system
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    Chapter 50 Symbiotic coevolution of artificial neural networks and training data sets
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    Chapter 51 Information-theoretic analysis of a mobile agent's learning in a discrete state space
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    Chapter 52 The coevolution of antibodies for concept learning
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    Chapter 53 Does data-model co-evolution improve generalization performance of evolving learners?
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    Chapter 54 A corporate classifier system
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    Chapter 55 Applying diffusion to a cooperative coevolutionary model
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    Chapter 56 Parallel Problem Solving from Nature — PPSN V
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    Chapter 57 Learning to avoid moving obstacles optimally for mobile robots using a genetic-fuzzy approach
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    Chapter 58 Evolutionary neural networks for nonlinear dynamics modeling
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    Chapter 59 Hybrid distributed real-coded genetic algorithms
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    Chapter 60 Mechanisms of emergent computation in cellular automata
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    Chapter 61 Towards designing neural network ensembles by evolution
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    Chapter 62 Selection of training data for neural networks by a genetic algorithm
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    Chapter 63 Discovery with genetic algorithm scheduling strategies for cellular automata
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    Chapter 64 Simple + parallel + local = cellular computing
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    Chapter 65 Evolution, learning and speech recognition in changing acoustic environments
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    Chapter 66 Ant colonies for adaptive routing in packet-switched communications networks
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    Chapter 67 The stud GA: A mini revolution?
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    Chapter 68 An island model based ant system with lookahead for the shortest supersequence problem
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    Chapter 69 Parameter-free Genetic Algorithm inspired by “disparity theory of evolution”
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    Chapter 70 Immune network dynamics for inductive problem solving
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    Chapter 71 Parallelization strategies for Ant Colony Optimization
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    Chapter 72 Self-organising pattern formation: Fruit flies and cell phones
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    Chapter 73 A new genetic local search algorithm for graph coloring
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    Chapter 74 Improving the performance of evolutionary algorithms for the satisfiability problem by refining functions
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    Chapter 75 Memetic algorithms and the fitness landscape of the graph bi-partitioning problem
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    Chapter 76 Investigating evolutionary approaches to adaptive database management against various quality of service metrics
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    Chapter 77 Genetic algorithm behavior in the MAXSAT domain
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    Chapter 78 An adaptive mutation scheme for a penalty-based graph-colouring GA
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    Chapter 79 Inver-over operator for the TSP
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    Chapter 80 Repair and brood selection in the traveling salesman problem
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    Chapter 81 The Traveling Salesrep Problem, Edge Assembly Crossover, and 2-opt
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    Chapter 82 Load balancing in parallel circuit testing with annealing-based and genetic algorithms
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    Chapter 83 A heuristic combination method for solving job-shop scheduling problems
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    Chapter 84 Reduction of air traffic congestion by genetic algorithms
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    Chapter 85 Timetabling the classes of an entire university with an evolutionary algorithm
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    Chapter 86 Genetic algorithms for the Multiple Container Packing Problem
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    Chapter 87 Buffer memory optimization in DSP applications: An evolutionary approach
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    Chapter 88 The Breeder Genetic Algorithm for frequency assignment
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    Chapter 89 A permutation based Genetic Algorithm for minimum span frequency assignment
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    Chapter 90 Comparison of Evolutionary Algorithms for design optimization
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    Chapter 91 Aspects of digital evolution: Evolvability and architecture
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    Chapter 92 Integrated facility design using an evolutionary approach with a subordinate network algorithm
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    Chapter 93 An evolutionary algorithm for synthesizing optical thin-film designs
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    Chapter 94 Implementing genetic algorithms with sterical constraints for protein structure prediction
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    Chapter 95 Optimal placements of flexible objects: An adaptive simulated annealing approach
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    Chapter 96 Encapsulated Evolution strategies for the determination of group contribution model parameters in order to predict thermodynamic properties
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    Chapter 97 Recombination operators for evolutionary graph drawing
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    Chapter 98 Optimisation of density estimation models with evolutionary algorithms
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    Chapter 99 Genetic algorithm in parameter estimation of nonlinear dynamic systems
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    Chapter 100 Optimizing web page layout using an annealed genetic algorithm as client-side script
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    Chapter 101 Solving the capacitor placement problem in a radial distribution system using an adaptive genetic algorithm
Overall attention for this book and its chapters
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Title
Parallel Problem Solving from Nature — PPSN V : 5th International Conference Amsterdam, The Netherlands September 27–30, 1998 Proceedings
Published by
Springer, Berlin, Heidelberg, January 1998
DOI 10.1007/bfb0056843
ISBNs
978-3-54-065078-2, 978-3-54-049672-4
Editors

Eiben, Agoston E., Schwefel, Hans-Paul, Schoenauer, Marc, Bäck, Thomas

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The data shown below were compiled from readership statistics for 63 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 2 3%
Denmark 1 2%
Germany 1 2%
Belgium 1 2%
Unknown 58 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 19 30%
Student > Master 8 13%
Student > Doctoral Student 6 10%
Researcher 5 8%
Professor 4 6%
Other 8 13%
Unknown 13 21%
Readers by discipline Count As %
Computer Science 21 33%
Engineering 13 21%
Mathematics 5 8%
Business, Management and Accounting 2 3%
Chemistry 2 3%
Other 4 6%
Unknown 16 25%