Human-based computation. Human-based computation (HBC) is a computer science technique in which a machine performs its function by outsourcing certain steps to humans. This approach uses differences in abilities and alternative costs between humans and computer agents to achieve symbiotic human-computer interaction. In traditional computation, a human employs a computer[1] to solve a problem; a human provides a formalized problem description and an algorithm to a computer, and receives a solution to interpret. Human-based computation frequently reverses the roles; the computer asks a person or a large group of people to solve a problem, then collects, interprets, and integrates their solutions. Early work[edit] Human-based computation (apart from the historical meaning of "computer") research has its origins in the early work on interactive evolutionary computation.
A concept of the automatic Turing test pioneered by Moni Naor (1996) is another precursor of human-based computation. Alternative terms[edit] Evolutionary algorithm. Evolutionary algorithms often perform well approximating solutions to all types of problems because they ideally do not make any assumption about the underlying fitness landscape; this generality is shown by successes in fields as diverse as engineering, art, biology, economics, marketing, genetics, operations research, robotics, social sciences, physics, politics and chemistry[citation needed].
In most real applications of EAs, computational complexity is a prohibiting factor. In fact, this computational complexity is due to fitness function evaluation. Fitness approximation is one of the solutions to overcome this difficulty. However, seemingly simple EA can solve often complex problems; therefore, there may be no direct link between algorithm complexity and problem complexity. A possible limitation [according to whom?] Implementation of biological processes[edit] Evolutionary algorithm types[edit] Related techniques[edit] Swarm algorithms, including: Genetic algorithm. The 2006 NASA ST5 spacecraft antenna.
This complicated shape was found by an evolutionary computer design program to create the best radiation pattern. Genetic algorithms find application in bioinformatics, phylogenetics, computational science, engineering, economics, chemistry, manufacturing, mathematics, physics, pharmacometrics and other fields. Methodology[edit] In a genetic algorithm, a population of candidate solutions (called individuals, creatures, or phenotypes) to an optimization problem is evolved toward better solutions. Each candidate solution has a set of properties (its chromosomes or genotype) which can be mutated and altered; traditionally, solutions are represented in binary as strings of 0s and 1s, but other encodings are also possible.
A typical genetic algorithm requires: a genetic representation of the solution domain,a fitness function to evaluate the solution domain. Initialization of genetic algorithm[edit] Selection[edit] Genetic operators[edit] Termination[edit] Neural network. An artificial neural network is an interconnected group of nodes, akin to the vast network of neurons in a brain.
Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one neuron to the input of another. For example, a neural network for handwriting recognition is defined by a set of input neurons which may be activated by the pixels of an input image. After being weighted and transformed by a function (determined by the network's designer), the activations of these neurons are then passed on to other neurons.
This process is repeated until finally, an output neuron is activated. This determines which character was read. Like other machine learning methods - systems that learn from data - neural networks have been used to solve a wide variety of tasks that are hard to solve using ordinary rule-based programming, including computer vision and speech recognition. Background[edit] History[edit] Farley and Wesley A. Models[edit] or both. Artificial neural network.