Hybrid Evolutionary Systems

Book

1: Hybrid Evolutionary Algorithms (Preface, Table of contents)
Editors: Crina Grosan, Ajith Abraham and Hisao Ishibuchi

2. Engineering Evolutionary Intelligent Systems (Preface)
Editors: Ajith Abraham, Crina Grosan and Witold Pedrycz

Both books are now under the final stages of production. We expect the books to be available in September/October 2007. Once again many thanks for making this book project a great success.

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First of all many thanks for your contributions and making this edited volume a real success! We have received 38 chapters for the proposed edited volume focused on 'Hybrid Intelligent Systems'. We have decided to compile two separate volumes as follows:

Hybrid Evolutionary Algorithms
Editors: Crina Grosan,Ajith Abraham and Hisao Ishibuchi

Engineering Evolutionary Intelligent Systems
Editors: Ajith Abraham, Crina Grosan and Witold Pedrycz

Following are the list of chapters received for the proposed volume (only titles are listed). If you are a contributor and if your chapter is not listed below please contact us by email as soon as possible.

A Novel Hybrid Algorithm for Function Optimization: Particle Swarm Assisted Incremental Evolution Strategy
A New Genetic Approach for Neural Network Design
Quantum-Inspired Evolutionary Algorithm for Numerical Optimization
Memetic Algorithms in Microlithography Parametric Optimization: Hybridization of Genetic Algorithms with a Local Optimizer for Bound-constrained Optimization Problems
Multitarus: Agent Communication Framework
Significance of Hybrid Evolutionary Computation for Ab Inito Protein Folding Prediction
Grid Environment for General Purpose Optimization System
On the Design of Large-scale Cellular Mobile Networks Using Multi-population Memetic Algorithms
A Neural-Genetic Technique for Coastal Engineering: Determining Wave-induced Seabed Liquefaction Depth
An Intuitive GA Approach to Repair the Crew Schedule During Irregular Operation
Application of Genetic Algorithms to the reduction of size of a neural network
Combination of Genetic Algorithms and Fuzzy Logic Systems
Cluster wise design of Takagi-Sugeno Approach of Fuzzy Logic Controller
A Hybrid Evolutionary Heuristic for Job Scheduling on Computational Grids
Enhanced evolutionary algorithms for MDO: a control engineering perspective
A Grammatical Genetic Programming Representation for Radial Basis Function Networks
Evolution of Inductive Self-organizing Networks
Hybrid Genetic Algorithm and Bacterial Foraging Approach for Intelligent PID Controller Tuning
Robust Parametric Image Registration using Genetic Algorithms and Newton Levenberg Marquardt
Genetic Algorithm- Particle Swarm Optimization Based PI Controller Tuning for Indirect Vector Control of Three Phase
Induction Motor
Clustering Gene-Expression Data: A Hybrid Approach that Iterates Between k-Means and Evolutionary Search
A Hybrid Genetic Algorithm for the Optimization of Distribution Networks
Lessons from evolving of Neural Networks
Designing Layers in Hierarchical Fuzzy Logic Systems using Genetic Algorithms
Automatic Generation of Fuzzy Inference Systems using Genetic Algorithm and Fuzzy Q-Learning
Pareto Evolutionary Algorithm Hybridized with Local Search for Biobjective TSP
SOFHEA: Hybrid evolutionary algorithm for multiple sequence alignment
Hybrid Evolutionary Algorithms and Clustering Search
Genetically Optimized Hybrid Fuzzy Neural Networks: Analysis and Design of Rule-based Multi-layer Perceptron Architectures
Genetically Optimized Self-organizing Neural Networks Based on Polynomial and Fuzzy Polynomial Neurons: Analysis and Design
Evolutionary Fuzzy Modelling for Drug Resistant HIV-1 Treatment Optimization
Particle Swarm Optimization with Mutation for High Dimensional Problems
An efficient nearest neighbor classifier
Enhancing Recursive Supervised Learning using Clustering and Combinatorial optimization (RSL-CC)
Recursive Pattern based Hybrid Supervised Training
Simple Guided Local Search with Small Mutations for the Traveling Salesman Problem
Evolutionary Approach to Rule Extraction from Neural Networks
A Hybrid Cellular Genetic Algorithm for the Capacitated Vehicle Routing Problem

All chapters are under review and we expect to finalize the reviews by the end of May 2006. Authors of accepted chapters will be notified sometime during June 2006.

Evolutionary Computation has become an important problem solving methodology among many researchers working in the area of computational intelligence. The population based collective learning process, self adaptation and robustness are some of the key features of evolutionary algorithms when compared to other global optimization techniques. Evolutionary computation has been widely accepted for solving several important practical applications in engineering, business, commerce etc. As we all know, the problems of the future will be more complicated in terms of complexity and data volume.

Hybridization of evolutionary algorithms is getting popular due to their capabilities in handling several real world problems involving complexity, noisy environment, imprecision, uncertainty and vagueness. A fundamental stimulus to the investigations of hybrid approach is the awareness that combined approaches will be necessary to solve some of the real world problems. This edited volume is targeted to present the latest state-of-the-art methodologies in 'Hybrid Evolutionary Systems'. Editors invite authors to submit their original and unpublished work that communicates current research on 'Hybrid Evolutionary Systems', regarding both the theoretical and methodological aspects, as well as various applications to many real world problems from science, technology, business or commerce.

* Hybridization of evolutionary algorithms with other global optimization techniques (simulated annealing, Tabu search, GRASP etc.)

* Hybridization of evolutionary algorithms with molecular computing (DNA computing and membrane computing)

* Integration of evolutionary computation with case-based reasoning, inductive logic programming, grammatical inference etc.

III. This volume is also oriented towards real world applications where a direct approach might fail.

The book is intended to be published in the Springer Verlag, Series - 'Studies in Computational Intelligence'. Please prepare the manuscript using the author guidelines and format given in the following link: Author Guidelines

Authors are invited to submit their original and unpublished work by email to <computational.intelligence@gmail.com>. Papers have to be no more than 40 pages length. All chapters will be peer - reviewed by three or more independent referees.

Authors Intention to Contribute (with an abstract): January 15, 2006 (closed)