Genetic Algorithms + Data Structures = Evolution ProgramsSpringer Science & Business Media, 2013 M03 9 - 387 pages Genetic algorithms are founded upon the principle of evolution, i.e., survival of the fittest. Hence evolution programming techniques, based on genetic algorithms, are applicable to many hard optimization problems, such as optimization of functions with linear and nonlinear constraints, the traveling salesman problem, and problems of scheduling, partitioning, and control. The importance of these techniques is still growing, since evolution programs are parallel in nature, and parallelism is one of the most promising directions in computer science. The book is self-contained and the only prerequisite is basic undergraduate mathematics. This third edition has been substantially revised and extended by three new chapters and by additional appendices containing working material to cover recent developments and a change in the perception of evolutionary computation. |
Contents
1 | |
5 | 30 |
Why Do They Work? | 45 |
Selected Topics | 57 |
Binary or Float? | 97 |
45 | 100 |
Fine Local Tuning | 107 |
The harvest problem | 114 |
Machine Learning | 267 |
Evolutionary Programming and Genetic Programming | 282 |
A Hierarchy of Evolution Programs | 289 |
5858 | 295 |
Evolution Programs and Heuristics | 307 |
Conclusions 329 | 328 |
Appendix B | 349 |
363 | |
Other editions - View all
Genetic Algorithms + Data Structures = Evolution Programs Zbigniew Michalewicz No preview available - 2014 |
Genetic Algorithms + Data Structures = Evolution Programs Zbigniew Michalewicz No preview available - 2011 |
Common terms and phrases
applied approach best individual C₁ Chapter chromosome chromosome representation classifier systems column complex components convergence cost crossover crossover operator data structures decoders defined discussed domain edges encoding eval evaluation function evolution process evolution program evolution strategies evolutionary algorithms evolutionary computation evolutionary programming evolutionary techniques example experiments feasible and infeasible feasible individual feasible solution fitness floating point GAMS genetic algorithm genetic operators GENETIC-2 GENOCOP global optimum graph heuristic implementation incorporate infeasible individuals infeasible solutions initial population integer iteration linear constraints matrix method minimize mutation mutation operator nodes numerical optimization objective function optimization problems optimum parameters parents path penalty functions performance pop_size probability procedure random number randomly repair algorithm replace represented robot runs scheduling problems schema search space selected sequence solution vector solve subtours tion tour transportation problem traveling salesman problem variables