Optimization techniques for solving complex problems
"Here, a team of international experts brings together core ideas for solving complex problems in optimization across a wide variety of real-world settings, including computer science, engineering, transportation, telecommunications, and bioinformatics. Part One: Covers methodologies for comple...
| Otros Autores: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Hoboken, N.J. :
Wiley
c2009.
|
| Colección: | Wiley series on parallel and distributed computing.
|
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009849092806719 |
Tabla de Contenidos:
- Part I: Methodologies for complex problem solving: Generating automatic projections by means of genetic programming
- Neural lazy local learning
- Optimization by using genetic algorithms with micropopulations
- Analyzing parallel cellular genetic algorithms
- Evaluating new advanced multiobjective metaheuristics
- Canonical metaheuristics for dynamic optimization problems
- Solving constrained optimization problems with hybrid evolutionary algorithms
- Optimization of time series using parallel, adaptive, and neural techniques
- Using reconfigurable computing to optimization of cryptographic algorithms
- Genetic algorithms, parallelism and reconfigurable hardware
- Divide and conquer: advanced techniques
- Tools for tree searches: branch-and-bound and A* algorithms
- Tools for tree searches: Dynamic programming
- Part II: Applications: Automatic search of behavior strategies in auctions
- Evolving rules for local time series prediction
- Metaheuristics in bioinformatics: DNA sequencing and reconstruction
- Optimal location of antennae in telecommunication networks
- Optimization of image processing algorithms using FPGAs
- Application of cellular automata algorithms to the parallel simulation of laser dynamics
- Dense stereo disparity from an artificial life standpoint
- Exact, metaheuristic, and hybrid approaches to multidimensional knapsack problems
- Greedy seeding and problem-specific operators for GAs solution of strip packing problems
- Solving the KCT problem: large scale neighborhood search and solution merging
- Experimental study of GA-based schedulers in dynamic distributed computing environments
- Remote optimization service (ROS)
- Remote services for advanced problem optimization.
