Genetic Algorithm Thomas Malthus' Essay on the Principles of Populations in 1798 is one of the earliest known works dealing with the modeling of biological adaptation. Thus, virtually all inheritance and genetic research has occurred in the 20th century. The first determines which members of a population survive to reproduce, and the second ensures mixing and recombination among the genes of their offspring. Genetic algorithms are patterned after natural genetic operators that enable biological populations to effectively and robustly adapt to their environment and to changes in their environment. Some of the correspondences between biological genetics and genetic algorithms are shown in Table 3.2.
Improvements come from trying new, risky things.
Because many of the risks fail, exploration involves a period of performance degradation. Deciding to what degree the present should be mortgaged for the future is a classic problem for all systems that adapt and learn. The competitive selection of structures for reproduction, based on each structure's fitness as a solution to the given problem. Idealized genetic operators that recombine the selected structures to create new structures for further testing. The following discusses the operators of a basic genetic algorithm.