Genetic Algorithms Attest to Evolution's Effectiveness in Complex Problem Solving

Despite what Intelligent Design proponents would have you believe, biological evolution is in fact very efficient at creating complex individuals that are uniquely suited to a large, complex, and even poorly understood environment. The basic principals of evolution--selection and mating between highly fit individuals over many generations to create more fit offspring with the occasional mutation--are used today by computer scientists to create genetic algorithms, which find numerical solutions to problems with many variables quickly and efficiently. The basic idea is that more fit parents selected from a random starting population produce more fit offspring to find the global optimum in a large array of possible solutions to a given problem. Occasional mutations prevent the overall population from falling into a local optimum, of which Darwin's Galapagos Islands might be an example. Genetic algorithms have been applied to many different types of problems from a range of fields including economics, ecology, networking, finance, and others. Contrary to the assertion that the universe is too complex to have been designed randomly without a creator, evolution is in fact quite good at creating a well functioning universe without anyone's help.