How Genetic Algorithms Mimic Evolution to Solve Complex Problems

Genetic algorithms were first proposed by Professor J. Holland of the University of Michigan in 1975 in his book "Adaptation in Natural and Artificial Systems". They are a class of randomized search algorithms inspired by natural selection and genetic mechanisms.

These algorithms simulate reproduction, crossover, and mutation occurring in natural selection. In each iteration a population of candidate solutions is maintained, and a certain metric is used to select the better individuals. Genetic operators (selection, crossover, mutation) combine these individuals to produce a new generation, repeating the process until a convergence criterion is met.

The primary evolutionary rule employed by genetic algorithms is "survival of the fittest", meaning better solutions are retained while poorer ones are eliminated.

The following cartoons illustrate the search mechanism of genetic algorithms:

Characteristics of Genetic Algorithms

(1) The search points in a GA population are parallel rather than single-point.

(2) GA uses probabilistic transformation rules instead of deterministic ones.

(3) The fitness function is not constrained by continuity or differentiability, making it widely applicable; only the objective function influencing search direction and its corresponding fitness function are needed.

(4) GA operates on encoded parameter sets rather than directly on the parameters themselves.