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Parallel Problem Solving from Nature — PPSN III

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Cover of 'Parallel Problem Solving from Nature — PPSN III'

Table of Contents

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    Book Overview
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    Chapter 245 Lamarckian evolution, the Baldwin effect and function optimization
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    Chapter 246 Control of parallel population dynamics by social-like behavior of GA-individuals
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    Chapter 247 Studying genotype-phenotype interactions: A model of the evolution of the cell regulation network
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    Chapter 248 A diploid genetic algorithm for preserving population diversity — Pseudo-Meiosis GA
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    Chapter 249 Co-evolutionary constraint satisfaction
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    Chapter 250 Towards a theory of ‘evolution strategies’: Results for (1 , + λ)-strategies on (nearly) arbitrary fitness functions
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    Chapter 251 Advanced correlation analysis of operators for the traveling salesman problem
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    Chapter 252 Genetic algorithms with multi-parent recombination
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    Chapter 253 On the mean convergence time of evolutionary algorithms without selection and mutation
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    Chapter 254 Estimating the heritability by decomposing the genetic variance
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    Chapter 255 Analyzing hyperplane synthesis in genetic algorithms using clustered schemata
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    Chapter 256 Convergence models of genetic algorithm selection schemes
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    Chapter 257 Optimal population size under constant computation cost
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    Chapter 258 An evolutionary algorithm for integer programming
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    Chapter 259 Long path problems
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    Chapter 260 Evolution strategies on noisy functions how to improve convergence properties
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    Chapter 261 Selection schemes with spatial isolation for genetic optimization
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    Chapter 262 A modified edge recombination operator for the Travelling Salesman Problem
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    Chapter 263 Step-size adaptation based on non-local use of selection information
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    Chapter 264 Strategy adaptation by competing subpopulations
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    Chapter 265 Controlling crossover through inductive learning
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    Chapter 266 Derivative operators for preference predicate evolution
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    Chapter 267 Adaptive crossover using automata
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    Chapter 268 Controlling dynamics of GA through filtered evaluation function
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    Chapter 269 A cooperative coevolutionary approach to function optimization
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    Chapter 270 A fuzzy classifier system using the Pittsburgh approach
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    Chapter 271 Q-learning in Evolutionary Rule Based Systems
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    Chapter 272 On the complexity of learning in classifier systems
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    Chapter 273 A representation scheme to perform program induction in a canonical genetic algorithm
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    Chapter 274 Genetic Programming with local hill-climbing
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    Chapter 275 Dynamic training subset selection for supervised learning in Genetic Programming
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    Chapter 276 Genotype-phenotype-mapping and neutral variation — A case study in Genetic Programming
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    Chapter 277 Genetic L-System Programming
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    Chapter 278 A genetic algorithm discovers particle-based computation in cellular automata
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    Chapter 279 Simulation of exaptive behaviour
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    Chapter 280 Artificial spacing patterns in a network of interacting celloids
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    Chapter 281 Diffuse pattern learning with Fuzzy ARTMAP and PASS
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    Chapter 282 Different learning algorithms for Neural Networks — A comparative study
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    Chapter 283 Program search with a hierarchical variable length representation: Genetic Programming, simulated annealing and hill climbing
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    Chapter 284 Problem-independent Parallel Simulated Annealing using selection and migration
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    Chapter 285 Parallel optimization of evolutionary algorithms
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    Chapter 286 Parallel simulated annealing and genetic algorithms: A space of hybrid methods
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    Chapter 287 ENZO-M — A hybrid approach for optimizing neural networks by evolution and learning
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    Chapter 288 Genetic lander: An experiment in accurate neuro-genetic control
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    Chapter 289 Effects of Occam's razor in evolving Sigma-Pi neural nets
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    Chapter 290 Designing neural networks by adaptively building blocks in cascades
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    Chapter 291 Hybrid adaptive heuristic critic architectures for learning in mazes with continuous search spaces
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    Chapter 292 Mutation operators for structure evolution of neural networks
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    Chapter 293 Implementation of standard genetic algorithm on MIMD machines
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    Chapter 294 Loosely coupled distributed genetic algorithms
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    Chapter 295 Applying Evolvable Hardware to autonomous agents
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    Chapter 296 Genetic Algorithms on LAN-message passing architectures using PVM: Application to the Routing problem
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    Chapter 297 Genetic algorithm based design optimization of CMOS VLSI circuits
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    Chapter 298 Improving evolutionary timetabling with delta evaluation and directed mutation
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    Chapter 299 Genetic improvement of railway timetables
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    Chapter 300 Using a genetic algorithm to search for the representational bias of a collective reinforcement learner
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    Chapter 301 An evolutionary algorithm for the routing of multi-chip modules
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    Chapter 302 System design under uncertainty: Evolutionary optimization of the Gravity Probe-B spacecraft
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    Chapter 303 Soft selection in D-optimal designs
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    Chapter 304 The weighted graph bi-partitioning problem: A look at GA performance
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    Chapter 305 RPL2: A language and parallel framework for evolutionary computing
Attention for Chapter 245: Lamarckian evolution, the Baldwin effect and function optimization
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Chapter title
Lamarckian evolution, the Baldwin effect and function optimization
Chapter number 245
Book title
Parallel Problem Solving from Nature — PPSN III
Published by
Springer, Berlin, Heidelberg, October 1994
DOI 10.1007/3-540-58484-6_245
Book ISBNs
978-3-54-058484-1, 978-3-54-049001-2
Authors

Darrell Whitley, V. Scott Gordon, Keith Mathias, Whitley, Darrell, Gordon, V. Scott, Mathias, Keith

Mendeley readers

The data shown below were compiled from readership statistics for 56 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 4%
United Kingdom 2 4%
Czechia 1 2%
France 1 2%
Spain 1 2%
United States 1 2%
Unknown 48 86%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 27%
Student > Ph. D. Student 15 27%
Student > Master 8 14%
Professor > Associate Professor 4 7%
Professor 4 7%
Other 7 13%
Unknown 3 5%
Readers by discipline Count As %
Computer Science 21 38%
Engineering 11 20%
Agricultural and Biological Sciences 5 9%
Business, Management and Accounting 2 4%
Biochemistry, Genetics and Molecular Biology 2 4%
Other 9 16%
Unknown 6 11%