## Complex systems: mechanism of adaptationThe last few years have seen an extraordinary growth in many areas of complex systems. In the field of synergetics and cooperative behaviour in neural systems a new vocabulary emerged to describe discoveries of wide-ranging and fundamental phenomena, like for example artificial life, biocomplexity, cellular automata, chaos, criticality, fractals, learning systems, neural networks, non-linear dynamics, parallel computation, percolation, self-organization.One of the contributing factors to this growth is the extraordinary increase in computing power. Previously intractable non-linear systems are now amenable to analysis and simulation and parallel computers are ever more important in these areas.The book contains papers exploring many aspects of complex systems, covering theory and applications and deal with material drawn from many different disciplines and specialities. |

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### Contents

Artificial Life Evolution and Complexity | 1 |

Evolution in Complex Systems | 21 |

Bootstrapping Evolution with ExtraSomatic Information | 29 |

Copyright | |

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### Common terms and phrases

adaptive affine transformations agents analysis application architecture artificial attractor ball behaviour binding biological cells cellular automata chaos chaos control chaotic colonies complex systems components computation configuration connected connectionist consider contraction maps convergence cortex defined described deterministic dilation maps dimension distribution dynamical systems Echo eigenvalues encoding Equation evolution evolutionary evolving example finite fitness function fixed point fractal dimension fractal sets function fuzzy rules gene genetic algorithms genotype geometry given global Hamming distance hash tagging hyphae initial input interaction iterations landscape layer learning machine matching mathematical method mutation rates neural network forecaster neurons node nonlinear optimisation output paper parallel parallel computer parameters path patterns peptides performance period PFSA phase pixel population possible prediction probability problem processors programming properties random region representation return maps selection sequence simple simulated annealing solution space species strategies structure technique unit variable vectors