Genetic algorithms

n·ving organisms are consummate problem solvers. They exhibit a ver satility that puts the best com puter programs to shame. This observa tion is especially galling for computer scientists, who may spend months or years of intellectual effort on an al gorithm, whereas organisms come by their abilities through the apparently undirected mechanism of evolution and natural selection. Pragmatic researchers see evolution's remarkable power as something to be emulated rather than envied. Natural selection eliminates one of the greatest hurdles in software design: specifying in advance all the features of a problem and the actions a program should take to deal with them. By harnessing the mechanisms of evolution, researchers may be able to "breed" programs that solve problems even when no person can fully understand their structure. In deed, these so-called genetic algorithms have already demonstrated the ability to make breakthroughs in the design of such complex systems as jet engines. Genetic algorithms make it possible to explore a far greater range of poten tial solutions to a problem than do con ventional programs. Furthermore, as re searchers probe the natural selection of programs under controlled and well-un-