III. Ecosmos: A Revolutionary Fertile, Habitable, Solar-Bioplanet Lifescape

5. Universal Evolution: A Celestial Expanse

Yang, Xin-She. Nature-Inspired Optimization Algorithms. Amsterdam: Elsevier, 2014. In our context of a worldwide learning project, the adoption of iterative methods such as Bayesian statistics, computational evolution, Markov processes, genetic algorithms, decision or game theory, and universal Darwinism seems to imply a dynamic cosmic development as some manner of self-selective maximization. This latest volume by the Middlesex University, London computer scientist and scholar provides as an extensive review of these mathematical programs at their generative work. After describing algorithmic programs, it goes on to biomimetic firefly, cuckoo search, bat, flower pollination, ant, bee, and particle swarm versions in use today. Their procedural operations are then perceived as a natural self-organization whence many agents interact by common rules to achieve a better fitness. To reflect, might we focus our own efforts to achieve a universe to humanity optimum? And could it all be a natural genetic code that emerges with evolution and meant to pass to our reception and continuance? Might one say therefore choose Earth?

In essence, an algorithm is a step-by-step procedure of providing calculations or instructions. Many algorithms are iterative. The actual steps and procedures depend on the algorithm used and the context of interest. However, in this book, we mainly concern ourselves with the algorithms for optimization, and thus we place more emphasis on iterative procedures for constructing algorithms. (1) In essence, a genetic algorithm (GA) is a search method based on the abstraction of Darwinian evolution and natural selection of biological systems and representing them in the mathematical operators: crossover or recombination, mutation, fitness, and selection of the fittest. (17)

A General Formula for Algorithms. Whatever the perspective, the aim of such an iterative process is to let the system evolve and converge into some stable optimality. In this case, it has strong similarity to a self-organizing system. Such an iterative, self-organizing system can evolve according to a set of rules or mathematical equations. As a result, such a complex system can interact and self-organize into certain converged states, showing some emergent characteristics of self-organization. In this sense, the proper design of an efficient optimization algorithm is equivalent to finding efficient ways to mimic the evolution of a self-organizing system. (176)

Heuristic: (Greek: "Εὑρίσκω", "find" or "discover") refers to experience-based techniques for problem solving, learning, and discovery that find a solution which is not guaranteed to be optimal, but good enough for a given set of goals. In computer science and mathematical optimization, a metaheuristic is a higher-level procedure or heuristic designed to find, generate, or select a lower-level procedure or heuristic (partial search algorithm) that may provide a sufficiently good solution to an optimization problem, especially with incomplete or imperfect information or limited computation capacity. (Wikipedia)

Yildiz, Izzet, et al. From Birdsong to Human Speech Recognition: Bayesian Inference on a Hierarchy of Nonlinear Dynamical Systems. PLoS Computational Biology. 9/9, 2013. Within our Rosetta Cosmos, Max Planck Institute for Human Cognitive and Brain Sciences researchers quantify that evolution has endowed both birds and people with similar modes of coded, informational sound transmission and neural reception. Once again, nature utilizes this same complex organization principles everywhere and everyone.

Our knowledge about the computational mechanisms underlying human learning and recognition of sound sequences, especially speech, is still very limited. One difficulty in deciphering the exact means by which humans recognize speech is that there are scarce experimental findings at a neuronal, microscopic level. Here, we show that our neuronal-computational understanding of speech learning and recognition may be vastly improved by looking at an animal model, i.e., the songbird, which faces the same challenge as humans: to learn and decode complex auditory input, in an online fashion. Motivated by striking similarities between the human and songbird neural recognition systems at the macroscopic level, we assumed that the human brain uses the same computational principles at a microscopic level and translated a birdsong model into a novel human sound learning and recognition model with an emphasis on speech. We show that the resulting Bayesian model with a hierarchy of nonlinear dynamical systems can learn speech samples such as words rapidly and recognize them robustly, even in adverse conditions. (Abstract)

As a model, we employ a novel Bayesian recognition method of dynamical sensory input such as birdsong and speech. The Bayesian approach first requires building of a so-called generative (internal) model, which is then converted to a learning and recognition model. The key advantage of this approach, as opposed to standard models in both human speech recognition and automatic speech recognition, is that the generative model is formulated as hierarchically structured, nonlinear dynamical systems. This means that one can employ generative models specifically tailored to birdsong or speech recognition. (2)

Zurek, Wojciech. Emergence of the Classical from Within the Quantum Universe. arXiv:2107.03378. The veteran LANL theoretical physicist (search) continues his collegial endeavor to finesse the essence of this deepest, fundamental realm. In some way a John Wheeler-like observer function is a necessary activity, which then involves an informational receive/record aspect. By a wider view, our Earthuman phenomenon seems to serve as a microcosmic agent of universal self-articulation, which yet remains an arduous process of to express what may be actually going on.

Decoherence shows how the openness of quantum systems -- interaction with their environment -- suppresses flagrant manifestations of quantumness. Einselection accounts for the emergence of preferred quasi-classical pointer states. Quantum Darwinism goes beyond decoherence. It posits that the information acquired by the monitoring environment responsible for decoherence is disseminated, in many copies, in the environment, and thus becomes accessible to observers. (Abstract excerpt)

Zurek, Wojciech. Quantum Theory of the Classical: Quantum Jumps, Born’s Rule, and Objective Classical Reality via Quantum Darwinism. arXiv:1807.02092. The LANL physicist and originator of the QD concept that even this basic phase forms many candidate states from which selections are made continues his project, with a growing number of advocates, to develop this considerable insight. It is couched in technical terms in need of editing and arrangement, but contributes to a global perception of a wholly evolutionary cosmos. See also Revealing the Emergence of Classicality in Nitrogen-Vacancy Centers by Thomas Undan, et al including Zurek at 1809.10456, and other entries (Paul Knott) herein.

The LANL physicist and originator of the QD concept that even this basic phase forms many candidate states from which selections are made continues his project, with a growing number of advocates, to develop this considerable insight. It is couched in technical terms in need of editing and arrangement, but contributes to a global perception of a wholly evolutionary cosmos. See also Revealing the Emergence of Classicality in Nitrogen-Vacancy Centers by Thomas Undan, et al including Zurek at 1809.10456, and other entries (Paul Knott) herein.

Zurek, Wojciech. Relative States and the Environment: Einselection, Envariance, Quantum Darwinism, and the Existential Interpretation.. arXiv:0707.2832v1.. Published online February 3, 2008 by the Los Alamos Laboratory physicist as the latest version of his collaborative theory that in relatively sub- or pre- biological realms of quantum phenomena, a selective process also goes on similar to that experienced by classical organisms. Google ‘Quantum Darwinism’ for its website, and see also Philip Ball "Quantum All the Way" in Nature (453/22, 2008). For a later update see Quantum Darwinism by Zurek in Physics Today for October 2014.

Objective existence can be acquired (via quantum Darwinism) only by a relatively small fraction of all degrees of freedom within the quantum Universe. The rest is needed to “keep records.” Clearly, there is only a limited (if large) memory space available for this at any time. This limitation on the total memory available means that not all quantum states that exist or quantum events that happen now “really happens” – only a small fraction of what occurs will be still in the records in the future. So the finite memory capacity of the Universe implies indefiniteness of the present and impermanence of the past: To sum it up, one can extend John Wheeler’s dictum “the past exists only insofar as it is recorded in the present” and say “whatever exists is there only insofar as it is recorded.” (26)

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