Lecture Notes in Computer Science

Global convergence of genetic algorithms: A markov chain analysis.- The theory of virtual alphabets.- Towards an optimal mutation probability for genetic algorithms.- An alternative Genetic Algorithm.- An analysis of the interacting roles of population size and crossover in genetic algorithms.- Gleam a system for simulated "intuitive learning".- Genetic algorithms and highly constrained problems: The time-table case.- An evolution standing on the design of redundant manipulators.- Redundant coding of an NP-complete problem allows effective Genetic Algorithm search.- Circuit partitioning with genetic algorithms using a coding scheme to preserve the structure of a circuit.- Genetic algorithms, production plan optimisation and scheduling.- System identification using genetic algorithms.- Conformational analysis of DNA using genetic algorithms.- Operator-oriented genetic algorithm and its application to sliding block puzzle problem.- A topology exploiting genetic algorithm to control dynamic systems.- Genetic local search algorithms for the traveling salesman problem.- Genetic programming artificial nervous systems artificial embryos and embryological electronics.- Concept formation and decision tree induction using the genetic programming paradigm.- On solving travelling salesman problems by genetic algorithms.- Genetic algorithms and punctuated equilibria in VLSI.- Implementing the genetic algorithm on transputer based parallel processing systems.- Explicit parallelism of genetic algorithms through population structures.- Parallel genetic packing of rectangles.- Partitioning a graph with a parallel genetic algorithm.- Solving the mapping-problem - Experiences with a genetic algorithm.- Optimization using distributed genetic algorithms.- Application of the Evolutionsstrategie to discrete optimization problems.- A variant of evolution strategies for vector optimization.- Application of evolution strategy in parallel populations.- Global optimization by means of distributed evolution strategies.- Solving sequential games with Boltzmann-learned tactics.- Optimizing simulated annealing.- Parallel Implementations Of Simulated Annealing / A local timing model for parallel optimization with Boltzmann Machines.- Error-free parallel implementation of simulated annealing.- Trimm: A parallel processor for image reconstruction by simulated annealing.- The response-time constraint in neural evolution.- An artificial neural network representation for artificial organisms.- Feature construction for back-propagation.- Improved convergence rate of back-propagation with dynamic adaption of the learning rate.- Performance evaluation of evolutionarily created neural network topologies.- Optical image preprocessing for neural network classifier system.- Gannet: Genetic design of a neural net for face recognition.- The application of a genetic approach as an algorithm for neural networks.- Genetic improvements of feedforward nets for approximating functions.- Exploring adaptive agency III: Simulating the evolution of habituation and sensitization.- A learning strategy for neural networks based on a modified evolutionary strategy.- Genetic algorithms and the immune system.- Selectionist categorization.- A classifier system with integrated genetic operators.- The fuzzy classifier system: Motivations and first results.- Hints for adaptive problem solving gleaned from immune networks.- A reactive robot navigation system based on a fluid dynamics metaphor.- Transfer of natural metaphors to parallel problem solving applications.- Modelling and simulation of distributed evolutionary search processes for function optimization.- Parallel, decentralized spatial mapping for robot navigation and path planning.- Ecological dynamics under different selection rules in distributed and iterated prisoner's dilemma game.- Adaptation in signal spaces.- A principle of minimum complexity in evolution.- The emergence of data structures from local interactions.- The view from the adaptive landscape.- Boltzmann-, Darwin- and Haeckel-strategies in optimization problems.- Optimizing complex problems by nature's algorithms: Simulated annealing and evolution strategy-a comparative study.- Genetic Algorithms and evolution strategies: Similarities and differences.- Building the ultimate machine: The emergence of artificial cognition.