CHAPTER BOOK: Evolutionary Algorithms based on te Automata Theory for the Multiobjective Optimization of Combinatorial Problems

Hi everyone:

This is my first chapter book, here is the information of the book:

Genetic Algorithm / Book 2 ISBN 979-953-307-879-2 The book covers the next topics:

* Evolutionary algorithms
* Evolutionary computing
* Metaheuristics
* Stochastic optimization
* Optimization
* Genetic representation
* Population growth estimation
* Web intelligence domains
* Pattern recognition
* Data mining
* Soft computing
* Bioinformatics
* Learning systems

It will be avaliable by January 2012. In this chapter I review all the techniques proposed by me and a novel metaheuristic is designed. Also, the tested are considered very hard, I work with instance from Bi to Quint-objectives. Here is a brief description of the chapter content:

1. Introduction. (Research regarding to Combinatorial Optimization)

2. Preliminares.
2.1. Multiobjective Optimization
2.2. Genetic Algorithms
2.3. Simulated Annealing Algorithms
2.4. Metaheuristic of Deterministic Swapping (MODS).

3. Simulated Annealing Metaheuristic of Deterministic Swapping (SAMODS)

4. Evolutionary Metaheuristic of Deterministic Swapping (EMODS)

5. Genetic Simulated Annealing Metaheuristic of Deterministic Swapping (SAGAMODS)

6. Experimental Analysis
6.1. Experimental Settings.
6.2. Performance Metrics (Generations of No Dominated Vector (GNDV), Spacing (S), Generational Distance (GD), Inverse Generational Distance (IGD)...)
6.3. Experimental Results
6.4. Analysis

7. Conclusions.

8. References.

A graphical comparison of the results for three objective instances is shown in the next figure: