Real WorldApplns. of Genetic Algorithms [appl. math]
โ Scribed by O. Roeva
- Publisher
- Intech
- Year
- 2012
- Tongue
- English
- Leaves
- 376
- Category
- Library
No coin nor oath required. For personal study only.
โฆ Table of Contents
00 preface_ Real-World Applications of Genetic Algorithms......Page 1
01_Different Tools on Multi-Objective
Optimization of a Hybrid Artificial Neural
Network โ Genetic Algorithm for Plasma
Chemical Reactor Modelling......Page 15
02_Application of Bio-Inspired Algorithms and
Neural Networks for Optimal Design of Fractal
Frequency Selective Surfaces......Page 41
03_Evolutionary Multi-Objective Algorithms......Page 67
04_Evolutionary Algorithms Based on the Automata
Theory for the Multi-Objective Optimization of
Combinatorial Problems......Page 95
05_Evolutionary Techniques in Multi-Objective
Optimization Problems in Non-Standardized
Production Processes......Page 123
06_A Hybrid Parallel Genetic Algorithm for
Reliability Optimization......Page 141
07_Hybrid Genetic Algorithm-Support Vector
Machine Technique for Power Tracing in
Deregulated Power Systems......Page 161
08_Hybrid Genetic Algorithm for
Fast Electromagnetic Synthesis......Page 179
09_A Hybrid Methodology Approach for Container
Loading Problem Using Genetic Algorithm to
Maximize the Weight Distribution of Cargo......Page 197
10_Hybrid Genetic Algorithms for
the Single Machine Scheduling Problem
with Sequence-Dependent Setup Times......Page 213
11_Genetic Algorithms and Group Method
of Data Handling-Type Neural Networks
Applications in Poultry Science......Page 233
12_New Approaches to Designing Genes by
Evolution in the Computer......Page 249
13_Application of Genetic Algorithms and Ant
Colony Optimization for Modelling of
E. coli Cultivation Process......Page 275
14_Multi-Objective Genetic Algorithm to
Automatically Estimating the Input Parameters
of Formant-Based Speech Synthesizers......Page 297
15_Solving Timetable Problem by Genetic
Algorithm and Heuristic Search Case Study:
Universitas Pelita Harapan Timetable......Page 317
16_Genetic Algorithms for Semi-Static
Wavelength-Routed Optical Networks......Page 331
17_Surrogate-Based Optimization......Page 357
๐ SIMILAR VOLUMES
The works presented in this book give insights into the creation of innovative improvements over algorithm performance, potential applications on various practical tasks, and combination of different techniques. The book provides a reference to researchers, practitioners, and students in both artifi
ะะทะดะฐัะตะปัััะฒะพ InTech, 2012, -376 pp.<div class="bb-sep"></div>Genetic Algorithms are a part of Evolutionary Computing, which is a rapidly growing area of Artificial Intelligence. The popularity of Genetic Algorithms is reflected in the increasing amount of literature devoted to theoretical works and
Explore the ever-growing world of genetic algorithms to solve search, optimization, and AI-related tasks, and improve machine learning models using Python libraries such as DEAP, scikit-learn, and NumPy Key Features โข Explore the ins and outs of genetic algorithms with this fast-paced guide โข I
<p><p>This book is the result of several years of research trying to better characterize parallel genetic algorithms (pGAs) as a powerful tool for optimization, search, and learning. Readers can learn how to solve complex tasks by reducing their high computational times. Dealing with two scientific