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An evolutionary algorithm (also EA, evolutionary computation, artificial evolution) is an algorithm using evolutionary techniques inspired by mechanisms from biological evolution such as natural selection, mutation and recombination to find an optimal configuration for a specific system within specific constraints. Evolutionary algorithms include:
- evolution strategy works with vectors of real numbers as representations of optimization problems; selection, mutation and adaption of mutation rates are important working mechanisms.
- genetic algorithms apply selection, mutation, and recombination operators to abstract representations of a problem, usually a binary string of a fixed length.
- genetic programming uses tree-like representations of computer programs and selection, mutation, and recombination operators defined over tree-like representation to optimize those programs
- evolutionary programming allows one to parameterize computer programs to find optimal solutions according to a goal function.
Most of these techniques are similar in spirit, but differ in the details of their implementation and the nature of the particular problem domains to which they have been applied. Evolutionary algorithms are traditionally used to design engineering systems, but have recently been applied to art, biology, economics, genetics, operations research, roboticsEvolutionary Robotics ER is a methodology that uses evolutionary algorithms to develop controllers for autonomous robots. It allows to automatically create robot controllers from an initially random population by selecting according to a predefined fitnes, social sciencesThe social sciences comprise the application of scientific methods to the study of the human aspects of the world. They are also known as social studies and (pejoratively) as the soft sciences''. Psychology studies the human mind and behavior; sociology e, and other fields. Delegation of some operators of evolutionary computation to outside agents (usually humans) is another recent trend known as Interactive evolutionary computationInteractive evolutionary computation (IEC) is a general term for methods of evolutionary computation that use human evaluation. Usually human evaluation is necessary when the form of fitness function is not known (for example, visual appeal or attractiven.
1 EC as framework for evolutionary modeling
Evolutionary computation and algorithms have also been used as an experimental framework within which to validate theories about evolution and natural selection, particularly through work in the field of artificial lifeArtificial life also known as alife is the study of life through the use of human-made analogs of living systems. Computer scientist Christopher Langton coined the term in the late 1980s when he held the first "International Conference on the Synthesis an. Techniques from evolutionary algorithms applied to the modelling of biological evolution a generally limited to explorations of microevolutionary processesMicroevolution is the occurrence of small-scale changes in gene frequencies in a population over a few generations, also known as change at or below the species level. These changes may be due to several processes: mutation, gene flow, genetic drift, as w, however some computer simulations, such as TierraTierra is a computer simulation developed by ecologist Thomas S. Ray in the early 1990s in which computer programs compete for central processor unit ( CPU) time and access to main memory. The computer programs in Tierra are evolvable and can mutate, self, attempt to model macroevolutionaryMacroevolution is the theory that evolution of species and higher taxa is the result of large-scale changes in gene-frequencies over time. It is distinct from microevolution, which is the sequence of changes that occur within a single population. Overview dynamics.
