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Full Description
The term evolutionary computing refers to the study of the foundations and applications of certain heuristic techniques based on the principles of natural evolution; thus the aim of designing evolutionary algorithms (EAs) is to mimic some of the processes taking place in natural evolution. These algo rithms are classified into three main categories, depending more on historical development than on major functional techniques. In fact, their biological basis is essentially the same. Hence EC = GA uGP u ES uEP EC = Evolutionary Computing GA = Genetic Algorithms,GP = Genetic Programming ES = Evolution Strategies,EP = Evolutionary Programming Although the details of biological evolution are not completely understood (even nowadays), there is some strong experimental evidence to support the following points: • Evolution is a process operating on chromosomes rather than on organ isms. • Natural selection is the mechanism that selects organisms which are well adapted to the environment toreproduce more often than those which are not. • The evolutionary process takes place during the reproduction stage that includes mutation (which causes the chromosomes of offspring to be dif ferent from those of the parents) and recombination (which combines the chromosomes of the parents to produce the offspring). Based upon these features, the previously mentioned three models of evolutionary computing were independently (and almost simultaneously) de veloped. An evolutionary algorithm (EA) is an iterative and stochastic process that operates on a set of individuals (called a population).
Contents
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.
