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This book includes a selection of twelve carefully revised papers chosen from the papers accepted for presentation at the 4th IEEE/Nagoya-University World Wisepersons Workshop held in Nagoya in November 1995.The combining of the technologies of fuzzy logic, neural networks, and evolutionary computation is expected to open up a new paradigm of machine learning for the realization of human-like information generating systems. The excellent papers presented are organized in sections on fuzzy and evolutionary computation, fuzzy and learning automata, fuzzy and neural networks, genetic algorithms, and CAM-brain.
(source: Nielsen Book Data)

• Computational brittleness and the evolution of computer viruses
• Evolutionary computing in multi-agent environments: Speciation and symbiogenesis
• Evolution strategies with subjective selection
• Emergent cooperation for multiple agents using genetic programming
• Evolution programs evolved
• Encoding scheme issues for open-ended artificial evolution
• Hardware evolution at function level
• Coevolutionary life-time learning
• Genetic programs and co-evolution
• Self-assemblage of gene nets in evolution via recruiting of new netters
• A survey of intron research in genetics
• Analytical and numerical investigations of evolutionary algorithms in continuous spaces
• On the asymptotic behavior of multirecombinant Evolution Strategies
• Are long path problems hard for genetic algorithms?
• Random tree generation for genetic programming
• Implicit formae in genetic algorithms
• A probabilistic database approach to the analysis of genetic algorithms
• Mean field analysis of tournament selection on a random manifold
• From recombination of genes to the estimation of distributions I. Binary parameters
• From recombination of genes to the estimation of distributions II. Continuous parameters
• Searching in the presence of noise
• The density of states
• A measure of the difficulty of optimisation problems
• On interactive evolutionary algorithms and stochastic mealy automata
• The influence of different coding schemes on the computational complexity of genetic algorithms in function optimization
• An analysis of the effects of neighborhood size and shape on local selection algorithms
• Evolutionary computation at the edge of feasibility
• Dimensional analysis of allele-wise mixing revisited
• Gaussian diffusion in a simple genetic algorithm
• Erroneous truncation selection
• A breeder's decision making perspective
• New crossover methods for sequencing problems
• The effect of extensive use of the mutation operator on generalization in genetic programming using sparse data sets
• On permutation representations for scheduling problems
• Multi-parent's niche: N-ary crossovers on NK-landscapes
• A preliminary investigation into directed mutations in evolutionary algorithms
• Heuristic crossovers for real-coded genetic algorithms based on fuzzy connectives
• Are evolutionary algorithms improved by large mutations?
• Mutation by imitation in boolean evolution strategies
• Formal algorithms + formal representations =search strategies
• A genetic algorithm with variable range of local search for tracking changing environments
• An Evolution Strategy with adaptation of the step sizes by a variance function
• Every niching method has its niche: Fitness sharing and implicit sharing compared
• Effects of isolation in a distributed population genetic algorithm
• Self-adaptive genetic algorithm for numeric functions
• Niche search: An evolutionary algorithm for global optimisation
• Adaptively parameterised evolutionary systems: Self adaptive recombination and mutation in a genetic algorithm
• Obtaining multiple distinct solutions with genetic algorithm niching methods
• Cost Based Operator Rate Adaptation: An investigation
• Genetic algorithms and relational landscapes
• IOGA: An instance-oriented genetic algorithm
• Explicit filtering of building blocks for genetic algorithms
• Multi-objective optimization by means of the thermodynamical genetic algorithm
• Adaptation to a changing environment by means of the thermodynamical genetic algorithm
• The development of a dual-agent strategy for efficient search across whole system engineering design hierarchies
• A parallel cellular genetic algorithm used in finite element simulation
• A robust solution searching scheme in genetic search
• Solving MasterMind using GAs and simulated annealing: A case of dynamic constraint optimization
• Evolving compact solutions in genetic programming: A case study
• Climbing up NP-hard hills
• On the performance assessment and comparison of stochastic multiobjective optimizers
• Paginating the generalized newspapers
• A comparison of simulated annealing and a heuristic method
• A comparison of optimization techniques for integrated manufacturing planning and scheduling
• A comparison of search techniques on a wing-box optimisation problem
• A comparative study of evolutionary algorithms for on-line parameter tracking
• Modeling urban growth by cellular automata
• Democratic optimization for discrete and continuous systems
• A study of some properties of Ant-Q
• Immunoid: An immunological approach to decentralized behavior arbitration of autonomous mobile robots
• Parallelizable evolutionary dynamics principles for solving the maximum clique problem
• Significance of locality and selection pressure in the grand deluge evolutionary algorithm
• Parallel computing with DNA: Toward the anti-universal machine
• Tackling the "curse of dimensionality" of radial basis functional neural networks using a genetic algorithm
• A Three-stage method for designing Genetic Fuzzy Systems by learning from examples
• Learning heuristics for OBDD minimization by Evolutionary Algorithms
• Improving the generalization performance of multi-layer-perceptrons with population-based incremental learning
• Robust GP in robot learning
• A pattern recognition system using evolvable hardware
• Topology design of feedforward neural networks by genetic algorithms
• An evolution strategy for on-line optimisation of dynamic objective functions
• Exploiting competing subpopulations for automatic generation of test sequences for digital circuits
• Constraint handling in evolutionary search: A case study of the frequency assignment
• An application of genetic algorithms and neural networks to scheduling power generating systems
• Evolutionary algorithms for the calculation of electron distributions in Si-MOSFETs
• Refueling of a nuclear power plant: Comparison of a naive and a specialized mutation operator
• Genetic algorithms applied to the physical design of VLSI circuits: A survey
• Stochastic methods for transistor size optimization of CMOS VLSI circuits
• An adaptive parallel Genetic Algorithm for VLSI-layout optimization
• Genetic algorithms for protocol validation
• Constraint handling for the fault coverage code generation problem: An inductive evolutionary approach
• New genetic local search operators for the traveling salesman problem
• An evolutionary approach to hardware/software partitioning
• Evolutionary Air Traffic Flow Management for large 3D-problems
• Genetic-based dynamic load balancing: Implementation and evaluation
• Production scheduling with genetic algorithms and simulation
• Network optimization using evolutionary strategies
• Co-evolving parallel random number generators
• Scheduling by genetic local search with multi-step crossover
• Finding the conformation of organic molecules with genetic algorithms
• Investigating a Parallel Breeder Genetic Algorithm on the inverse Aerodynamic design
• An evolutionary design for f-? lenses
• Optimization of heat exchanger networks by means of evolution strategies
• Industrial plant pipe-route optimisation with genetic algorithms
• An evolutionary algorithm for design optimization of microsystems
• A learning classifier system for three-dimensional shape optimization.

This book constitutes the refereed proceedings of the International Conference on Evolutionary Computation held jointly with the 4th Conference on Parallel Problem Solving from Nature, PPSN IV, in Berlin, Germany, in September 1996. The 103 revised papers presented in the volume were carefully selected from more than 160 submissions. The papers are organized in sections on basic concepts of evolutionary computation (EC), theoretical foundations of EC, modifications and extensions of evolutionary algorithms, comparison of methods, other metaphors, and applications of EC in a variety of areas like ML, NNs, engineering, CS, OR, and biology. The book has a comprehensive subject index.

This book constitutes the refereed proceedings of the International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR'97, held in Venice, Italy, in May 1997.The book presents 29 revised full papers selected from a total of 62 submissions. Also included are four full invited papers and a keynote paper by leading researchers. The volume is organized in sections on contours and deformable models, Markov random fields, deterministic methods, object recognition, evolutionary search, structural models, and applications. The volume is the first comprehensive documentation of the application of energy minimization techniques in the areas of compiler vision and pattern recognition.
(source: Nielsen Book Data)

• Simulating pricing behaviours using a genetic algorithm / Sue Bedingfield, Stephen Huxford, Yen Cheung
• Biologically inspired computational ecologies : a case study / Paul Devine, Ray Paton
• Modelling bounded rationality using evolutionary techniques / Bruce Edmonds, Scott Moss
• The abstract theory of evolution of the living / V.L. Kalmykov
• An evolutionary algorithm for single objective nonlinear constrained optimization problems / Hunter P. Albright, James P. Ignizio
• On recombinative sampling / Ian East
• The evolution of mutation, plasticity and culture in cyclically changing environments ; On the structure and transformation of landscapes / Tony Hirst
• Island model genetic algorithms and linearly separable problems / Darrell Whitley, Soraya Rana, Robert Heckendorn
• Empirical validation of the performance of a class of transient detector / Philip Jacob, Andrew Ball
• The construction and evaluation of decision trees : a comparison of evolutionary and concept learning methods / H. Kennedy ... [et al.]
• Parallel distributed genetic programming applied to the evolution of natural language recognisers / Riccardo Poli
• Scheduling planned maintenance of the South Wales region of the national grid / Bill Langdon
• Solving generic scheduling problems with a distributed genetic algorithm / Malcolm McIlhagga
• Directing the search of evolutionary and neighbourhood-search optimisers for the flowshop sequencing problem with an idle-time heuristic / Peter Ross, Andrew Tuson
• Multiobjective genetic algorithms for pump scheduling in water supply / Dragan Savic, Godfrey Walters, Martin Schwab
• Use of rules and preferences for schedule builders in genetic algorithms for production scheduling / Jane Shaw, Peter Fleming
• A voxel based approach to evolutionary shape optimisation / Peter Baron ... [et al.]
• Phase transition networks : a modelling technique supporting the evolution of autonomous agents' tactical and operational activities / Anthony Deakin, Derek Yates
• An evolutionary, agent-assisted strategy for conceptual design space decomposition / Ian Parmee, M. Beck
• Task scheduling with use of classifier systems / Franciszek Seredýnski.

This book constitutes the refereed post-workshop proceedings of the AISB International Workshop on Evolutionary Computing, held in Manchester, UK, in April 1997.The 22 strictly reviewed and revised full papers presented were selected for inclusion in the book after two rounds of refereeing. The papers are organized in sections on evolutionary approaches to issues in biology and economics, problem structure and finite landscapes, evolutionary machine learning and classifier systems, evolutionary scheduling, and more techniques and applications of evolutionary algorithms.
(source: Nielsen Book Data)

This book constitutes the strictly refereed post-conference proceedings recording the scientific progress achieved at the First International Conference on Evolvable Systems: From Biology to Hardware, ICES'96, held in Tsukuba, Japan, in October 1996. The volume presents 33 revised full papers including several invited contributions surveying the state of the art in this emerging area of research and development. The volume is divided into topical sections on evolware, cellular systems, engineering applications of evolvable hardware systems, evolutionary robotics, innovative architectures, evolvable systems, evolvable hardware, and genetic programming.
(source: Nielsen Book Data)

This book constitutes the refereed proceedings of the Second International Conference on Evolvable Systems: From Biology to Hardware, ICES '98, held in Lausanne, Switzerland in September 1998. The 38 revised papers presented were carefully selected for inclusion in the book from numerous submissions. The papers are organized in topical sections on evaluation of digital systems, evolution of analog systems, embryonic electronics, bio-inspired systems, artifical neural networks, adaptive robotics, adaptive hardware platforms, and molecular computing.
(source: Nielsen Book Data)

• Modelling genetic algorithms: From Markov chains to dependence with complete connections
• On the optimization of unimodal functions with the (1+1) evolutionary algorithm
• A timing analysis of convergence to fitness sharing equilibrium
• Where elitists start limping evolution strategies at ridge functions
• A bit-wise epistasis measure for binary search spaces
• Inside GA dynamics: Ground basis for comparison
• The effect of spin-flip symmetry on the performance of the simple GA
• Fitness distance correlation and Ridge functions
• Accelerating the convergence of evolutionary algorithms by fitness landscape approximation
• Modeling building-block interdependency
• Mutate large, but inherit small! On the analysis of rescaled mutations in ()-ES with noisy fitness data
• Creating robust solutions by means of evolutionary algorithms
• Analytic curve detection from a noisy binary edge map using genetic algorithm
• A comparison of dominance mechanisms and simple mutation on non-stationary problems
• Adaptation to a changing environment by means of the feedback thermodynamical genetic algorithm
• Optimization with noisy function evaluations
• On risky methods for local selection under noise
• Polygenic inheritance
• A haploid scheme that can outperform diploidy
• Averaging efficiently in the presence of noise
• Solving binary constraint satisfaction problems using evolutionary algorithms with an adaptive fitness function
• Varying fitness functions in genetic algorithms: Studying the rate of increase of the dynamic penalty terms
• Landscape changes and the performance of Mapping Based Constraint handling methods
• A decoder-based evolutionary algorithm for constrained parameter optimization problems
• A spatial predator-prey approach to multi-objective optimization: A preliminary study
• Selective breeding in a multiobjective genetic algorithm
• Niching and elitist models for MOGAs
• Parallel evolutionary optimisation with constraint propagation
• Methods to evolve legal phenotypes
• Multiobjective optimization using evolutionary algorithms
• A comparative case study
• Utilizing dynastically optimal forma recombination in hybrid genetic algorithms
• Further experimentations on the scalability of the GEMGA
• Indexed memory as a generic protocol for handling vectors of data in genetic programming
• On genetic algorithms and lindenmayer systems
• Genome length as an evolutionary self-adaptation
• Restart scheduling for genetic algorithms
• A comparative study of global and local selection in evolution strategies
• UEGO, an abstract niching technique for global optimization
• Development of problem-specific evolutionary algorithms
• The effects of control parameters and restarts on search stagnation in evolutionary programming
• Accelerating the evolutionary-gradient-search procedure: Individual step sizes
• Extending population-based incremental learning to continuous search spaces
• Multi-parent recombination in genetic algorithms with search space boundary extension by mirroring
• Selective crossover in genetic algorithms: An empirical study
• Line-breeding schemes for combinatorial optimization
• Finding regions of uncertainty in learned models: An application to face detection
• On ZCS in multi-agent environments
• Empirical analysis of the factors that affect the Baldwin effect
• Promoting generalisation of learned behaviours in genetic programming
• Generalization in Wilson's classifier system
• Symbiotic coevolution of artificial neural networks and training data sets
• Information-theoretic analysis of a mobile agent's learning in a discrete state space
• The coevolution of antibodies for concept learning
• Does data-model co-evolution improve generalization performance of evolving learners?
• A corporate classifier system
• Applying diffusion to a cooperative coevolutionary model
• Studying parallel evolutionary algorithms: The cellular programming case
• Learning to avoid moving obstacles optimally for mobile robots using a genetic-fuzzy approach
• Evolutionary neural networks for nonlinear dynamics modeling
• Hybrid distributed real-coded genetic algorithms
• Mechanisms of emergent computation in cellular automata
• Towards designing neural network ensembles by evolution
• Selection of training data for neural networks by a genetic algorithm
• Discovery with genetic algorithm scheduling strategies for cellular automata
• Simple + parallel + local = cellular computing
• Evolution, learning and speech recognition in changing acoustic environments
• Ant colonies for adaptive routing in packet-switched communications networks
• The stud GA: A mini revolution?
• An island model based ant system with lookahead for the shortest supersequence problem
• Parameter-free Genetic Algorithm inspired by "disparity theory of evolution"
• Immune network dynamics for inductive problem solving
• Parallelization strategies for Ant Colony Optimization
• Self-organising pattern formation: Fruit flies and cell phones
• A new genetic local search algorithm for graph coloring
• Improving the performance of evolutionary algorithms for the satisfiability problem by refining functions
• Memetic algorithms and the fitness landscape of the graph bi-partitioning problem
• Investigating evolutionary approaches to adaptive database management against various quality of service metrics
• Genetic algorithm behavior in the MAXSAT domain
• An adaptive mutation scheme for a penalty-based graph-colouring GA
• Inver-over operator for the TSP
• Repair and brood selection in the traveling salesman problem
• The Traveling Salesrep Problem, Edge Assembly Crossover, and 2-opt
• Load balancing in parallel circuit testing with annealing-based and genetic algorithms
• A heuristic combination method for solving job-shop scheduling problems
• Reduction of air traffic congestion by genetic algorithms
• Timetabling the classes of an entire university with an evolutionary algorithm
• Genetic algorithms for the Multiple Container Packing Problem
• Buffer memory optimization in DSP applications: An evolutionary approach
• The Breeder Genetic Algorithm for frequency assignment
• A permutation based Genetic Algorithm for minimum span frequency assignment
• Comparison of Evolutionary Algorithms for design optimization
• Aspects of digital evolution: Evolvability and architecture
• Integrated facility design using an evolutionary approach with a subordinate network algorithm
• An evolutionary algorithm for synthesizing optical thin-film designs
• Implementing genetic algorithms with sterical constraints for protein structure prediction
• Optimal placements of flexible objects: An adaptive simulated annealing approach
• Encapsulated Evolution strategies for the determination of group contribution model parameters in order to predict thermodynamic properties
• Recombination operators for evolutionary graph drawing
• Optimisation of density estimation models with evolutionary algorithms
• Genetic algorithm in parameter estimation of nonlinear dynamic systems
• Optimizing web page layout using an annealed genetic algorithm as client-side script
• Solving the capacitor placement problem in a radial distribution system using an adaptive genetic algorithm.

This book constitutes the refereed proceedings of the 5th International Conference on Parallel Problem Solving from Nature, PPSN V, held in Amsterdam, The Netherlands, in September 1998. The 101 papers included in their revised form were carefully reviewed and selected from a total of 185 submissions. The book is divided into topical sections on convergence theory; fitness landscape and problem difficulty; noisy and non-stationary objective functions; multi-criteria and constrained optimization; representative issues; selection, operators, and evolution schemes; coevolution and learning; cellular automata, fuzzy systems, and neural networks; ant colonies, immune systems, and other paradigms; TSP, graphs, and satisfiability; scheduling, partitioning, and packing; design and telecommunications; and model estimations and layout problems.

This book constitutes the refereed proceedings of the Second International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR'99, held in York, UK in July 1999.The book presents 11 revised full papers together with 11 papers presented at the meeting as posters. Those papers were selected from a total of 33 submissions. The book is divided in sections on shape, minimum description length, Markov random fields, contours, search and consistent labeling, tracking and video, and biomedical applications.
(source: Nielsen Book Data)

This book consitutes the refereed joint proceedings of the First European Workshop on Evolutionary Computation in Image Analysis and Signal Processing, EvoIASP '99 and of the First European Workshop on Evolutionary Telecommunications, EuroEcTel '99, held in Goteborg, Sweden in May 1999. The 18 revised full papers presented were carefully reviewed and selected for inclusion in the volume. The book presents state-of-the-art research results applying techniques from evolutionary computing in the specific application areas.
(source: Nielsen Book Data)