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Stochastic optimization (SO) are optimization methods that generate and use random variables. For stochastic optimization problems, the objective functions or constraints are random. Stochastic optimization also include methods with random iterates .
Stochastic diffusion search (SDS) was first described in 1989 as a population-based, pattern-matching algorithm. [1] It belongs to a family of swarm intelligence and naturally inspired search and optimisation algorithms which includes ant colony optimization, particle swarm optimization and genetic algorithms; as such SDS was the first Swarm Intelligence metaheuristic.
Beam search is a modification of best-first search that reduces its memory requirements. Best-first search is a graph search which orders all partial solutions (states) according to some heuristic. But in beam search, only a predetermined number of best partial solutions are kept as candidates. [1] It is thus a greedy algorithm.
Stochastic forensics analyzes computer crime by viewing computers as stochastic steps. In artificial intelligence , stochastic programs work by using probabilistic methods to solve problems, as in simulated annealing , stochastic neural networks , stochastic optimization , genetic algorithms , and genetic programming .
A stochastic simulation is a simulation of a system that has variables that can change stochastically (randomly) ... and is a linear search on the cumulative array.
A stochastic program is an optimization problem in which some or all problem parameters are uncertain, but follow known probability distributions. [ 1 ] [ 2 ] This framework contrasts with deterministic optimization, in which all problem parameters are assumed to be known exactly.
Stochastic computing is a collection of techniques that represent continuous values by streams of random bits. Complex computations can then be computed by simple bit-wise operations on the streams. Complex computations can then be computed by simple bit-wise operations on the streams.
An important subclass are the local search methods, that view the elements of the search space as the vertices of a graph, with edges defined by a set of heuristics applicable to the case; and scan the space by moving from item to item along the edges, for example according to the steepest descent or best-first criterion, or in a stochastic search.