Evolutionary optimization algorithms : biologically-Inspired and population-based approaches to computer intelligence
Evolutionary optimization algorithms : biologically-Inspired and population-based approaches to computer intelligence
复制标题
DOI：
--
复制
发表时间：
2013-04
期刊：
影响因子：
--
通讯作者：
D. Simon
中科院分区：
文献类型：
--
作者：
D. Simon
Acknowledgments xxi Acronyms xxiii List of Algorithms xxvii Pert I: Introduction to Evolutionary Optimization 1 Introduction 1 2 Optimization 11 Part II: Classic Evoluntionary Algorithms 3 Generic Algorithms 35 4 Mathematical Models of Genetic Algorithms 63 5 Evolutionary Programming 95 6 Evolution Strategies 117 7 Genetic Programming 141 8 Evolutionary Algorithms Variations 179 Part III: More Recent Evolutionary Algorithms 9 Simulated Annealing 223 10 Ant Colony Optimization 241 11 Particle Swarm Optimization 265 12 Differential Evolution 293 13 Estimation of Distribution Algorithms 313 14 Biogeography-Based Optimization 351 15 Cultural Algorithms 377 16 Oppostion-Based Learning 397 17 Other Evolutionary Algorithms 421 Part IV: Special Type of Optimization Problems 18 Combinatorial Optimization 449 19 Constrained Optimization 481 20 Multi-Objective Optimization 517 21 Expensive, Noisy and Dynamic Fitness Functions 563 Appendices A Some Practical Advice 607 B The No Free Luch Therorem and Performance Testing 613 C Benchmark Optimization Functions 641
