Evolutionary design of intelligent systems is gaining much popularity due to its capabilities in handling several real world problems involving optimization, complexity, noisy and non-stationary environment, imprecision, uncertainty and vagueness. This edited volume 'Engineering Evolutionary Intelligent Systems' deals with the theoretical and methodological aspects, as well as various evolutionary algorithm applications to many real world problems originating from science, technology, business or commerce. This volume comprises of 15 chapters including an introductory chapter which covers the fundamental definitions and outlines some important research challenges. Chapters were selected on the basis of fundamental ideas/concepts rather than the thoroughness of techniques deployed.

Reports recent research results on Engineering Evolutionary Intelligent Systems

Engineering Evolutionary Intelligent Systems: Methodologies, Architectures and Reviews.- 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.- Evolution of Inductive Self-organizing Networks.- Recursive Pattern based Hybrid Supervised Training.- Enhancing Recursive Supervised Learning Using Clustering and Combinatorial Optimization (RSL-CC).- Evolutionary Approaches to Rule Extraction from Neural Networks.- Cluster-wise Design of Takagi and Sugeno Approach of Fuzzy Logic Controller.- Evolutionary Fuzzy Modelling for Drug Resistant HIV-1 Treatment Optimization.- A New Genetic Approach for Neural Network Design.- A Grammatical Genetic Programming Representation for Radial Basis Function Networks.- A Neural-Genetic Technique for Coastal Engineering: Determining Wave-induced Seabed Liquefaction Depth.- On the Design of Large-scale Cellular Mobile Networks Using Multi-population Memetic Algorithms.- A Hybrid Cellular Genetic Algorithm for the Capacitated Vehicle Routing Problem.- Particle Swarm Optimization with Mutation for High Dimensional Problems.