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. [ edit ] Early work
In artificial intelligence , an evolutionary algorithm (EA) is a subset of evolutionary computation , a generic population-based metaheuristic optimization algorithm . An EA uses mechanisms inspired by biological evolution , such as reproduction , mutation , recombination , and selection . Candidate solutions to the optimization problem play the role of individuals in a population, and the fitness function determines the environment within which the solutions "live" (see also cost function ). Evolution of the population then takes place after the repeated application of the above operators.
In the computer science field of artificial intelligence , a genetic algorithm (GA) is a search heuristic that mimics the process of natural evolution. This heuristic is routinely used to generate useful solutions to optimization and search problems . Genetic algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance , mutation , selection , and crossover . Genetic algorithms find application in bioinformatics , phylogenetics , computational science , engineering , economics , chemistry , manufacturing , mathematics , physics , pharmacometrics and other fields.
Simplified view of a feedforward artificial neural network The term neural network was traditionally used to refer to a network or circuit of biological neurons . [ 1 ] The modern usage of the term often refers to artificial neural networks , which are composed of artificial neurons or nodes. Thus the term may refer to either biological neural networks are made up of real biological neurons or artificial neural networks for solving artificial intelligence problems. Unlike von Neumann model computations, artificial neural networks do not separate memory and processing and operate via the flow of signals through the net connections, somewhat akin to biological networks. These artificial networks may be used for predictive modeling, adaptive control and applications where they can be trained via a dataset.
An artificial neural network , often just named a neural network , is a mathematical model inspired by biological neural networks . A neural network consists of an interconnected group of artificial neurons , and it processes information using a connectionist approach to computation . In most cases a neural network is an adaptive system changing its structure during a learning phase. Neural networks are used for modeling complex relationships between inputs and outputs or to find patterns in data. An artificial neural network is an interconnected group of nodes, akin to the vast network of neurons in a brain . [ edit ] Background