Parallel Problem Solving from Nature

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.