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Computers > Artificial Intelligence > Genetic Programming > Resources > EA vs. GA vs. EP vs. GP
EA vs. GA vs. EP vs. GP
Submitted 2005-10-03 19:57:01 by psiolent
Rating: 0 (0 votes) | 2739 Views
Section: Resources
Genetic programming, evolutionary computation, evolutionary algorithms...what are the differences? The Hitch-Hiker's Guide to Evolutionary Computation explains:
Evolutionary algorithm: "an umbrella term used to describe computer-based problem solving systems which use computational models of some of the known mechanisms of evolution as key elements in their design and implementation."
Genetic algorithm: "a model of machine learning which derives its behavior from a metaphor of some of the mechanisms of evolution in nature. This is done by the creation within a machine of a population of individuals represented by chromosomes, in essence a set of character strings that are analogous to the base-4 chromosomes that we see in our own DNA. The individuals in the population then go through a process of simulated 'evolution'."
Evolutionary programming: "originally conceived by Lawrence J. Fogel in 1960, is a stochastic optimization strategy similar to genetic algorithms, but instead places emphasis on the behavioral linkage between parents and their offspring, rather than seeking to emulate specific genetic operators as observed in nature."
Genetic programming: "the extension of the genetic model of learning into the space of programs. That is, the objects that constitute the population are not fixed-length character strings that encode possible solutions to the problem at hand, they are programs that, when executed, 'are' the candidate solutions to the problem." |
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