Advances in Evolutionary Computing

This volume presents the state of art of research on theory and applications in the field of Evolutionary Computation. It contains contributions of established scientists of the field and maintains a balance of theory and applications. It will be useful for researchers, scientists, professionals, teachers and students interested in the the field; there are non-competitive books.

State of the art of theory and applications in Evolutionary Algorithms Contributions by established researchers in the field Well-balanced between theory and applications Includes supplementary material: sn.pub/extras

Texte du rabat

The term evolutionary computing (EC) refers to the study of the foundations and applications of certain heuristic techniques based on the principles of natural evolution, and thus the aim when designing evolutionary algorithms (EAs) is to mimic some of the processes taking place in natural evolution.

Many researchers around the world have been developing EC methodologies for designing intelligent decision-making systems for a variety of real-world problems. This book provides a collection of 40 articles, written by leading experts in the field, containing new material on both the theoretical aspects of EC and demonstrating its usefulness in various kinds of large-scale real-world problems. Of the articles contributed, 23 articles deal with various theoretical aspects of EC and 17 demonstrate successful applications of EC methodologies.

Contenu
I.- Smoothness, Ruggedness and Neutrality of Fitness Landscapes: from Theory to Application.- Fast Evolutionary Algorithms.- Visualizing Evolutionary Computation.- New Schemes of Biologically Inspired Evolutionary Computation.- On the Design of Problem-specific Evolutionary Algorithms.- Multiparent Recombination in Evolutionary Computing.- TCG-2: A Test-case Generator for Non-linear Parameter Optimisation Techniques.- A Real-coded Genetic Algorithm Using the Unimodal Normal Distribution Crossover.- Designing Evolutionary Algorithms for Dynamic Optimization Problems.- Multi-objective Evolutionary Algorithms: Introducing Bias Among Pareto-optimal Solutions.- Gene Expression and Scalable Genetic Search.- Solving Permutation Problems with the Ordering Messy Genetic Algorithm.- Effects of Adding Perturbations to Phenotypic Parameters in Genetic Algorithms for Searching Robust Solutions.- Evolution of Strategies for Resource Protection Problems.- A Unified Bayesian Framework for EvolutionaryLearning and Optimization.- Designed Sampling with Crossover Operators.- Evolutionary Computation for Evolutionary Theory.- Computational Embryology: Past, Present and Future.- An Evolutionary Approach to Synthetic Biology: Zen in the Art of Creating Life.- Scatter Search.- The Ant Colony Optimization Paradigm for Combinatorial Optimization.- Evolving Coordinated Agents.- Exploring the Predictable.- II.- Approaches to Combining Local and Evolutionary Search for Training Neural Networks: A Review and Some New Results.- Evolving Analog Circuits by Variable Length Chromosomes.- Human-competitive Applications of Genetic Programming.- Evolutionary Algorithms for the Physical Design of VLSI Circuits.- From Theory to Practice: An Evolutionary Algorithm for the Antenna Placement Problem.- Routing Optimization in Corporate Networks by Evolutionary Algorithms.- Genetic Algorithms and Timetabling.- Machine Learning by Schedule Decomposition Prospects for an Integration of AI and OR Techniquesfor Job Shop Scheduling.- Scheduling of Bus Drivers' Service by a Genetic Algorithm.- A Survey of Evolutionary Algorithms for Data Mining and Knowledge Discovery.- Data Mining from Clinical Data Using Interactive Evolutionary Computation.- Learning-integrated Interactive Image Segmentation.- An Immunogenetic Approach in Chemical Spectrum Recognition.- Application of Evolutionary Computation to Protein Folding.- Evolutionary Generation of Regrasping Motion.- Recent Trends in Learning Classifier Systems Research.- Better than Samuel: Evolving a Nearly Expert Checkers Player.