Real-World Applications of Genetic Algorithms

Olympia Roeva

• Publisher: IntechOpen
• Year: 2012
• ISBN:
  9789535156895
• Topic: Informatica
• Subtopic: Reti neurali

The book addresses some of the most recent issues, with the theoretical and methodological aspects, of evolutionary multi-objective optimization problems and the various design challenges using different hybrid intelligent approaches. Multi-objective optimization has been available for about two decades, and its application in real-world problems is continuously increasing. Furthermore, many applications function more effectively using a hybrid systems approach. The book presents hybrid techniques based on Artificial Neural Network, Fuzzy Sets, Automata Theory, other metaheuristic or classical algorithms, etc. The book examines various examples of algorithms in different real-world application domains as graph growing problem, speech synthesis, traveling salesman problem, scheduling problems, antenna design, genes design, modeling of chemical and biochemical processes etc.

Table of contents

• Real-World Applications of Genetic Algorithms
• Contents
• Preface
• Chapter 1 Different Tools on Multi-Objective Optimization of a Hybrid Artificial Neural Network – Genetic Algorithm for Plasma Chemical Reactor Modelling
• Chapter 2 Application of Bio-Inspired Algorithms and Neural Networks for Optimal Design of Fractal Frequency Selective Surfaces
• Chapter 3 Evolutionary Multi-Objective Algorithms
• Chapter 4 Evolutionary Algorithms Based on the Automata Theory for the Multi-Objective Optimization of Combinatorial Problems
• Chapter 5 Evolutionary Techniques in Multi-Objective Optimization Problems in Non-Standardized Production Processes
• Chapter 6 A Hybrid Parallel Genetic Algorithm for Reliability Optimization
• Chapter 7 Hybrid Genetic Algorithm-Support Vector Machine Technique for Power Tracing in Deregulated Power Systems
• Chapter 8 Hybrid Genetic Algorithm for Fast Electromagnetic Synthesis
• Chapter 9 A Hybrid Methodology Approach for Container Loading Problem Using Genetic Algorithm to Maximize the Weight Distribution of Cargo
• Chapter 10 Hybrid Genetic Algorithms for the Single Machine Scheduling Problem with Sequence-Dependent Setup Times
• Chapter 11 Genetic Algorithms and Group Method of Data Handling- Type Neural Networks Applications in Poultry Science
• Chapter 12 New Approaches to Designing Genes by Evolution in the Computer
• Chapter 13 Application of Genetic Algorithms and Ant Colony Optimization for Modelling of E. coli Cultivation Process
• Chapter 14 Multi-Objective Genetic Algorithm to Automatically Estimating the Input Parameters of Formant-Based Speech Synthesizers
• Chapter 15 Solving Timetable Problem by Genetic Algorithm and Heuristic Search Case Study: Universitas Pelita Harapan Timetable
• Chapter 16 Genetic Algorithms for Semi-Static Wavelength-Routed Optical Networks
• Chapter 17 Surrogate-Based Optimization