10 Facts About Genetic algorithms

Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover and selection.

Genetic algorithms are simple to implement, but their behavior is difficult to understand.

In particular, it is difficult to understand why these Genetic algorithms frequently succeed at generating solutions of high fitness when applied to practical problems.

The notion of real-valued genetic algorithms has been offered but is really a misnomer because it does not really represent the building block theory that was proposed by John Henry Holland in the 1970s.

Coarse-grained parallel genetic algorithms assume a population on each of the computer nodes and migration of individuals among the nodes.

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Fine-grained parallel genetic algorithms assume an individual on each processor node which acts with neighboring individuals for selection and reproduction.

Problems which appear to be particularly appropriate for solution by genetic algorithms include timetabling and scheduling problems, and many scheduling software packages are based on GAs.

Genetic algorithms are often applied as an approach to solve global optimization problems.

Examples of problems solved by genetic algorithms include: mirrors designed to funnel sunlight to a solar collector, antennae designed to pick up radio signals in space, walking methods for computer figures, optimal design of aerodynamic bodies in complex flowfields.

Genetic algorithms's work originated with studies of cellular automata, conducted by Holland and his students at the University of Michigan.