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Ch. 1 | Co-evolutionary learning in strategic environments | 1 |
Ch. 2 | Using evolution to learn user preferences | 20 |
Ch. 3 | A parallel genetic algorithm for clustering | 41 |
Ch. 4 | Using SIMD genetic programming for fault-tolerant trading strategies | 61 |
Ch. 5 | An efficient coevolutionary algorithm based on merging and splitting of species | 78 |
Ch. 6 | Schema analysis of genetic algorithms on multiplicative landscape | 93 |
Ch. 7 | Evolutionary learning strategies for artificial life characters | 112 |
Ch. 8 | Adaptive strategy for GA inspired by biological evolution | 132 |
Ch. 9 | The influence of stochastic quality functions on evolutionary search | 152 |
Ch. 10 | Theoretical analysis of the GA performance with a multiplicative royal road function | 173 |
Ch. 11 | A real-coded cellular genetic algorithm inspired by predator-prey interactions | 191 |
Ch. 12 | Observed dynamics of large scale parallel evolutionary algorithms with implications for protein engineering | 208 |
Ch. 13 | Using edge histogram models to solve flow shop scheduling problems with probabilistic model-building genetic algorithms | 230 |
Ch. 14 | Collective movements of mobile robots with behavior models of a fish | 250 |
Ch. 15 | Automatic modularization with speciated neural network ensemble | 268 |
Ch. 16 | Search engine development using evolutionary computation methodologies | 284 |
Ch. 17 | Evaluating evolutionary multi-objective optimization algorithms using running performance metrics | 307 |
Ch. 18 | Visualization technique for analyzing non-dominant pareto optimality | 327 |
Ch. 19 | Image classification using particle swarm optimization | 347 |
Ch. 20 | A coevolutionary genetic search for a layout problem | 366 |
Ch. 21 | Sensitivity analysis in multi-objective evolutionary design | 386 |
Ch. 22 | Integrated production and transportation scheduling in supply chain optimisation | 406 |
Ch. 23 | Evolution of fuzzy rule based controllers for dynamic environments | 426 |
Ch. 24 | Applications of evolution algorithms to the synthesis of single/dual-rail mixed PTL/static logic for low-power applications | 446 |
Ch. 25 | Evolutionary multi-objective robotics : evolving a physically simulated quadruped using the PDE algorithm | 466 |
Ch. 26 | Applying Bayesian networks in practical customer satisfaction studies | 486 |
Ch. 27 | An adaptive length chromosome hyper-heuristic genetic algorithm for a trainer scheduling problem | 506 |
Ch. 28 | Design optimization of permanent magnet synchronous machine using genetic algorithms | 526 |
Ch. 29 | A genetic algorithm for joint optimization of spare capacity and delay in self-healing network | 542 |
Ch. 30 | Optimization of DS-CDMA code sequences for wireless systems | 562 |
Ch. 31 | An efficient evolutionary algorithm for multicast routing with multiple QoS constraints | 581 |
Ch. 32 | Constrained optimization of multilayered anti-reflection coatings using genetic algorithms | 603 |
Ch. 33 | Sequential construction of features based on genetically transformed data | 623 |
Ch. 34 | Refrigerant leak prediction in supermarkets using evolved neural networks | 643 |
Ch. 35 | Worst-case instances and lower bounds via genetic algorithms | 662 |
Ch. 36 | Prediction of protein secondary structure by multi-modal neural network | 682 |
Ch. 37 | Joint attention in the mimetic context - what is a "mimetic same"? | 698 |
Ch. 38 | Autonomous symbol acquisition through agent communication | 711 |
Ch. 39 | Search of steady-state genetic algorithms for vision-based mobile robots | 729 |
Ch. 40 | Time series forecast with Elman neural networks and genetic algorithms | 747 |
Ch. 41 | Co-adaption to facilitate naturalistic human involvement in shared control system | 769 |
Ch. 42 | Distributed evolutionary strategies for searching Oligo sets of yeast genome | 789 |
Ch. 43 | Duration-dependent multi-schedule evolutionary curriculum timetabling | 803 |
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Add Recent Advances in Simulated Evolution and Learning, Inspired by the Darwinian framework of evolution through natural selection and adaptation, the field of evolutionary computation has been growing very rapidly, and is today involved in many diverse application areas. This book covers the latest advances i, Recent Advances in Simulated Evolution and Learning to the inventory that you are selling on WonderClubX
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Add Recent Advances in Simulated Evolution and Learning, Inspired by the Darwinian framework of evolution through natural selection and adaptation, the field of evolutionary computation has been growing very rapidly, and is today involved in many diverse application areas. This book covers the latest advances i, Recent Advances in Simulated Evolution and Learning to your collection on WonderClub |