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Chemical Reaction Optimization CRO Nature-inspired Physics/Chemistry-based 2009 [28] Bat Algorithm BA Nature-inspired Bio-inspired 2010 [29] Charged System Search CSS Nature-inspired Physics/Chemistry-based 2010 [30] Eagle Strategy ES Nature-inspired 2010 Fireworks Algorithm FWA 2010 [31] Cuckoo Optimization Algorithm COA Nature-inspired Bio ...
The process that led to the algorithm recognizes several important steps. In 1931, Andrei Kolmogorov introduced the differential equations corresponding to the time-evolution of stochastic processes that proceed by jumps, today known as Kolmogorov equations (Markov jump process) (a simplified version is known as master equation in the natural sciences).
In theoretical chemistry, chemists, physicists, and mathematicians develop algorithms and computer programs to predict atomic and molecular properties and reaction paths for chemical reactions. Computational chemists, in contrast, may simply apply existing computer programs and methodologies to specific chemical questions.
Bacterial colony optimization; Barzilai-Borwein method; Basin-hopping; Benson's algorithm; Berndt–Hall–Hall–Hausman algorithm; Bin covering problem; Bin packing problem; Bland's rule; Branch and bound; Branch and cut; Branch and price; Bregman Lagrangian; Bregman method; Broyden–Fletcher–Goldfarb–Shanno algorithm
SGLD can be applied to the optimization of non-convex objective functions, shown here to be a sum of Gaussians. Stochastic gradient Langevin dynamics (SGLD) is an optimization and sampling technique composed of characteristics from Stochastic gradient descent, a Robbins–Monro optimization algorithm, and Langevin dynamics, a mathematical extension of molecular dynamics models.
CAOS aims to identify a series of chemical reactions which, from a starting compound, can produce a desired molecule. CAOS algorithms typically use two databases: a first one of known chemical reactions and a second one of known starting materials (i.e., typically molecules available commercially). Desirable synthetic plans cost less, have high ...
Reactive search optimization focuses on combining machine learning with optimization, by adding an internal feedback loop to self-tune the free parameters of an algorithm to the characteristics of the problem, of the instance, and of the local situation around the current solution. Genetic algorithms maintain a pool of solutions rather than ...
In the field of computational chemistry, energy minimization (also called energy optimization, geometry minimization, or geometry optimization) is the process of finding an arrangement in space of a collection of atoms where, according to some computational model of chemical bonding, the net inter-atomic force on each atom is acceptably close to zero and the position on the potential energy ...