How and where are genetic algorithms applied?
Originally defined by the creator John Holland, the goal of genetic algorithm was to abstract and rigorously explain the adaptive processes of natural systems and to develop ways in which natural adaptation might aid computer systems and software. This lead to important discoveries in both the natural and artificial realms. The central theme in artificial systems is: robustness, as defined by David E. Goldberg. Robustness is the “balance between efficiency and effacacy necessary for survival in varied environments.” GAs are computationally simple yet remarkably powerful. Genetic Algorithms are best applied to complex problems that are not easily solved through conventional means. They are used in solving problems in the areas of cellular automata, fuzzy logic, simulation and modeling, scheduling and optimization, network topology and routing, NP-Complete algorithms, in conjunction with neural networks, artificial life and robotic manipulation. They have been utilized in computer simula
