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-**symmetry breaking** (algorithmic technique)
+<blockquote>
+symmetry breaking (algorithmic technique)  To differentiate parts of a structure, such as a graph, which locally look the same to all vertices. Usually implemented with randomization.
-Definition: To differentiate parts of a structure, such as a graph, which locally look the same to all vertices. Usually implemented with randomization.
+https://www.nist.gov/dads/HTML/symmetrybrek.html
+</blockquote>
-Algorithms and Theory of Computation Handbook, CRC Press LLC, 1999, "symmetry breaking", in Dictionary of Algorithms and Data Structures [online], Vreda Pieterse and Paul E. Black, eds. 19 April 2004. (accessed TODAY) Available from: https://www.nist.gov/dads/HTML/symmetrybrek.html
+<blockquote>
+antichain (definition) A subset of mutually incomparable elements in a poset.
-**antichain** (definition)
+https://www.nist.gov/dads/HTML/antichain.html
+</blockquote>
-Definition: A subset of mutually incomparable elements in a poset (A set the elements of which are subject to a partial order)
+<blockquote>
+Bloom filter (data structure) A data structure with a probabilistic algorithm to quickly test membership in a large set using multiple hash functions into a single array of bits.
-Algorithms and Theory of Computation Handbook, CRC Press LLC, 1999, "antichain", in Dictionary of Algorithms and Data Structures [online], Vreda Pieterse and Paul E. Black, eds. 17 December 2004. (accessed TODAY) Available from: https://www.nist.gov/dads/HTML/antichain.html
+https://www.nist.gov/dads/HTML/bloomFilter.html
+</blockquote>
+<blockquote>
+Simulated annealing (SA) is a probabilistic technique for approximating the global optimum of a given function. Specifically, it is a metaheuristic to approximate global optimization in a large search space for an optimization problem. It is often used when the search space is discrete (e.g., all tours that visit a given set of cities). For problems where finding an approximate global optimum is more important than finding a precise local optimum in a fixed amount of time, simulated annealing may be preferable to alternatives such as gradient descent.
+The name and inspiration come from annealing in metallurgy, a technique involving heating and controlled cooling of a material to increase the size of its crystals and reduce their defects. Both are attributes of the material that depend on its thermodynamic free energy. Heating and cooling the material affects both the temperature and the thermodynamic free energy. The simulation of annealing can be used to find an approximation of a global minimum for a function with a large number of variables"
+https://en.wikipedia.org/wiki/Simulated_annealing
+</blockquote>
+<blockquote>
+Hydrological Cycle Algorithm (HCA) simulates nature’s hydrological water cycle. More specically, it involves a collection of water drops passing through different phases such as flow (runoff), evaporation, condensation, and precipitation to generate a solution. It can be considered as a swarm intelligence optimization algorithm for some parts of the cycle when a collection of water drops moves through the search space. But it can also be considered an evolutionary algorithm for other parts of the cycle when information is exchanged and shared. By using the full hydrological water cycle as a conceptual framework, we show that previous water-based algorithms have predominantly only used swarm-like aspects inspired by precipitation and flow. HCA, however, uses all four stages that will form a complete water-based approach to solving optimization problems effciently. In particular, we show that for certain problems HCA leads to improved performance and solution quality.
+https://doi.org/10.1155/2017/3828420
+</blockquote>
+<blockquote>
+The field of meta-heuristic search algorithms has a long history of finding inspiration in natural systems. Starting from classics such as Genetic Algorithms and Ant Colony Optimization, the last two decades have witnessed a fireworks-style explosion (pun intended) of natural (and sometimes supernatural) heuristics - from Birds and Bees to Zombies and Reincarnation.
+https://github.com/fcampelo/EC-Bestiary
+</blockquote>
+<blockquote>
+So, image for a moment an object, a material, which can literally do anything. It can move across categorical boundaries with no difficulty whatsoever. So what do I mean? I mean that if you possess the philosopher's stone and you were hungry, you could eat it. If you needed to go somewhere you could spread it out and sit on it and it would take you there. If you needed a piece of information, it would become the equivalent of a computer screen and it would tell you things. If you needed a companion, it would talk to you. If you needed to take a shower you could hold it over your head and water would pour out. Now, you see, this is an impossibility. That's right, it's a coincidencia apositorum. It is something that behaves like imagination and matter without ever doing damage to the ontological status of one or the other. This sounds like pure pathology in the context of modern thinking because we expect things to stay still and be what they are and undergo the growth and degradation that is inimical to them, but no, the redemption of spirit and matter means the exteriorization of the human soul and the interiorization of the human body so that it is an image freely commanded in the imagination.
+—Terence McKenna, Lectures on Alchemy
+</blockquote>
+<blockquote>
+When you cook bread from a recipe, you’re following an algorithm. When you knit a sweater from a pattern, you’re following an algorithm. When you put a sharp edge on a piece of flint by executing a precise sequence of strikes with the end of an antler—a key step in making fine stone tools—you’re following an algorithm. Algorithms have been a part of human technology ever since the Stone Age.
+—Christian & Griffiths
+</blockquote>
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   * Thacker, E. In the dust of this planet
   * NIST. Dictionary of Algorithms and Data Structures. https://www.nist.gov/dads/
+  * Sonic Acts. Living Earth
+  * Sonic Acts. The Geologic Imagination
+  * Brian Christian and Tom Griffiths. Algorithms to Live By: The Computer Science of Human Decisions,