| Evolutionary Computation |
Article Index for Evolutionary |
Shopping Evolutionary |
Website Links For Evolutionary |
Information AboutEvolutionary Computation |
| CATEGORIES ABOUT EVOLUTIONARY COMPUTATION | |
| evolutionary computationevolutionary computation | |
| evolution | |
| computation | |
| theoretical computer science | |
|
Evolutionary computation uses iterative progress, such as growth or development in a Population . This population is then Selected in a guided Random search using Parallel Processing to achieve the desired end. Such processes are often inspired by biological mechanisms of Evolution . HISTORY In the fifties, long before computers were used on a great scale, the idea to use Darwinian principles for automated problem solving originated. In the Sixties , three different interpretations of this idea have been developed at three different places. Evolutionary Programming was introduced by Lawrence J. Fogel in the USA , while John Henry Holland called his method a Genetic Algorithm . In Germany Ingo Rechenberg and Hans-Paul Schwefel introduced Evolution Strategies . These areas developed separately for about 15 years. From the early Nineties they are seen as different representatives (“dialects”) of one technology, called evolutionary computing. In the early nineties, another fourth stream following the general ideas had emerged – Genetic Programming . These terminologies denote the whole field by evolutionary computing and consider evolutionary programming, evolution strategies, genetic algorithms, and genetic programming as sub-areas. TECHNIQUES Evolutionary techniques mostly involve Metaheuristic Optimization Algorithm s such as:
and in a lesser extent also:
EVOLUTIONARY ALGORITHMS See Also: evolutionary algorithm Evolutionary Algorithms form a subset of evolutionary computation in that it generally only involve techniques implementing mechanisms inspired by Biological Evolution such as Reproduction , Mutation , Recombination , Natural Selection and Survival Of The Fittest . Candidate Solutions to the optimization problem play the role of individuals in a population, and the Cost Function determines the environment within which the solutions "live" (see also Fitness Function ). Evolution of the population then takes place after the repeated application of the above operators. In this process, there are two main forces that form the basis of evolutionary systems: Recombination and '''mutation''' create the necessary diversity and thereby facilitate novelty, while '''selection''' acts as a force increasing quality. Many aspects of such an evolutionary process are stochastic. Changed pieces of information due to recombination and mutation are randomly chosen. On the other hand, selection operators can be either deterministic, or stochastic. In the latter case, individuals with a higher fitness have a higher chance to be selected than individuals with a lower fitness, but typically even the weak individuals have a chance to become a parent or to survive. EVOLUTIONARY COMPUTATION PRACTITIONERS MAJOR CONFERENCES AND WORKSHOPS
JOURNALS
SEE ALSO
BIBLIOGRAPHY
REFERENCES
|
|
|