War of the Weasels An Evolutionary Algorithm Beats Intelligent Design How an intelligent design theorist was bested in a public math competition by a genetic algorithm—a computer simulation of evolution. DAVE THOMAS n the summer of 2006, a different kind of war was waged on the Internet—a war between computer programs writ - Iten by both evolutionary scientists and by intelligent design (ID) advocates. The war came to a climax in a public math competition in which dozens of humans stepped for - ward to compete against each other and against genetic (“evo - lutionary”) computer algorithms. The results were stunning: The official representative of the intelligent design commu - nity was outperformed by an evolutionary algorithm, thus learning Orgel’s Second Law—“Evolution is smarter than you are”—the hard way. In addition, the same IDer’s attempt to make a genetic algorithm that achieved a specific target without “specification” of that target was publicly exposed as 42 Volume 34, Issue 3 SKEPTICAL INQUIRER a rudimentary sham. And finally, two pillars of ID theory, get. However, this precise specification was used only for a “irreducible complexity” and “complex specified information” tutorial demonstration of the power of cumulative selection rather were shown not to be beyond the capabilities of evolution, than for generation of true novelty. In the Dawkins example, contrary to official ID dogma. the known target is the phrase from Hamlet, “Methinks it is like a weasel.” The organisms are initially random strings of Genetic Algorithms twenty-eight characters each. Every generation is tested, and “Genetic algorithms” (GAs) are computerized simulations of the string that is closest to the target Weasel phrase is selected evolution. They are used to study evolutionary processes and to seed the subsequent generation. The exact Shakespearean solve difficult (and sometimes intractable) design or analysis quote is obtained in just a few dozen generations. Despite problems. Several novel designs generated with genetic algo - Dawkins’s explicit disclaimer that, in real life, evolution has no rithms have been patented (Brainz.org 2008). Evolutionary long-distance target, creationists of all varieties have latched on algorithms are currently used in a variety of industries to get to “Weasel” as a convenient straw version of evolution that is effective answers to very difficult problems, including problems easy to poke holes in. whose brute-force solutions would require centuries , even on The main ID theorist dealing with genetic algorithms is superfast computers. In contrast, GAs can often produce highly William Dembski, who stated the ID/creationist position as of useful results for the same problems in just a few minutes. September 2005 with these words: The basic idea for a genetic algorithm is simple. You start And nevertheless, it remains the case that no genetic algorithm with a randomly generated “herd” of possible solutions to a or evolutionary computation has designed a complex, multipart, given difficult problem, where the general structure of any functionally integrated, irreducibly complex system without stack - conceivable solution can be represented with a chunk of mem - ing the deck by incorporating the very solution that was supposed to be attained from scratch (Dawkins 1986 and Schneider 2000 ory in a computer program. Treat the members of this herd as are among the worst offenders here). (Dembski 2005) “organisms,” and test every herd member’s performance with a fitness function . While the fitness function can be written in Stephen Meyer is a top gun in the Discovery Institute’s terms of proximity to a distant known “target,” it is more often Center for Science and Culture, the Seattle-based center of ID just a straightforward calculation of some parameter of inter - pontification and promotion. In Meyer’s “peer-reviewed” ID est, such as the length or cost of some component or feature, paper, “The Origin of Biological Information and the Higher or perhaps the gain of a wire antenna. Any candidate organism Taxonomic Categories,” he states: can have its fitness readily measured, and the performances of Genetic algorithms . only succeed by the illicit expedient of any number of candidates can be impartially compared. The providing the computer with a target sequence and then treat - fitness test is commonly used to help decide which organisms ing relatively greater proximity to future function (i.e., the tar - get sequence), not actual present function, as a selection crite - get to be “parents” for the next generation of organisms. rion. (Meyer 2004) Throwing in some mutations, and letting higher-fitness organ - isms breed for a few hundred generations, often leads to sur - Both Dembski and Meyer cite Weasel in these statements prising (and sometimes even astonishing) results. and go on to claim that all GAs are similarly targeted. And that Creationists and intelligent design proponents vigorously is the gist of the formal ID response to genetic algorithms: deny the fact that genetic algorithms demonstrate how the paint them all with the Weasel brush, and pretend they all evolution of novel and complex “designs” can happen. They need predefined targets to work. claim that GAs cannot generate true novelty and that all such Steiner’s Problem “answers” are surreptitiously introduced into the program via the algorithm’s fitness testing functions. The support for this In 2001, as I was preparing a response to an upcoming talk by claim stems mainly from a few pages of a book Richard ID’s Phillip Johnson at the University of New Mexico, I decided Dawkins wrote nearly twenty-five years ago. to address the Weasel problem. I set out to develop a genetic algorithm of my own for solving difficult math problems, with - Dawkins and the Weasel out using any specified target. I wanted something visual yet sim - Creationists have been fixated for decades on Richard ple—a sort of miniature digital playground on the very edge of Dawkins’s “Weasel” simulation from his 1986 book The Blind complexity. I ended up choosing “Steiner’s Problem”: given a Watchmaker (Dawkins 1986). Unlike real genetic algorithms two-dimensional set of points, find the most compact network of developed for industry or research, Dawkins’s Weasel algo - straight-line segments that connects the points (Courant and rithm included a very precise description of the intended tar - Hilbert 1941). In Steiner’s problem, there can be variable “Steiner points” in addition to the fixed points that are to be connected. If Dave Thomas, a physicist and mathematician, is president of there are four fixed points arranged in a rectangle, the Steiner New Mexicans for Science and Reason and a fellow of the solution consists of five segments connected in a bowtie shape; Committee for Skeptical Inquiry. He is currently a scientist/pro - each of the points on the rectangle’s corners connects to one of grammer at IRIS/PASSCAL in Socorro, New Mexico. E-mail: two Steiner points in the interior of the rectangle, and a fifth [email protected]. segment connects the two Steiner points (figure 1). SKEPTICAL INQUIRER May / June 2010 43 Figure 3: Two organisms shown as phenotypes (candidate networks after DNA transcription). While both organisms connect all fixed points, the right-hand creature is shorter and therefore more likely to be selected for breeding. Figure 1: The bowtie, the Steiner solution for four fixed points (solid) The Cyber Battles Begin A Genetic Algorithm for Steiner’s Problem I posted a detailed discussion of this work on the Panda’s In my Steiner genetic algorithm, the organisms are represented Thumb blog (www.pandasthumb.org) on July 5, 2006. The by strings of letters and numbers—a kind of primitive point of that report was to demonstrate that genetic algo - “DNA.” Two such DNA strands are shown in figure 2. The rithms can solve difficult problems without knowing anything strands, when read by the transcription routine, supply three about the answer(s) in advance. I demonstrated that, while types of information about the network represented by each occasionally producing the correct (Steiner) solution, most of organism: the number of Steiner points, the numerical loca - the time the algorithm converged on imperfect solutions. I tions of these points, and a true/false connection map that dic - called these “MacGyver” solutions, after the television hero tates which points are to be connected by segments. who often found clever ways to get out of tough fixes. While the MacGyver solutions are clearly not the optimum Steiner shape, they get the job done efficiently and are often within one percent of the length of the formal Steiner solution itself. The GA operates by seeding the next generation with those organisms that are shorter in length in the current generation. This GA does not , as Meyer falsely claims, select for future function (a precise target) rather than for present function (here, the lengths of the digital creatures). Figure 2: Two organisms represented by alphanumeric DNA The ID community responded to my article by simply reit - erating their claim that the solutions were secretly introduced Steiner points can be placed anywhere in the region encom - via the fitness function. IDers are desperate to make Dawkins’s passing the fixed points; for these simulations, the region is a Weasel the poster boy for all GAs, and they continue to paint square with 999 units on a side. Length is measured in these all GAs as similarly “target-driven” or “front-loaded.” Some ID units; for example, the length of the horizontal segment join - theorists have tried to skirt the obvious lack of specific target ing points (550,600) and (650,600) is 100 units. description in the Steiner genetic algorithm by claiming that Some representative networks for a six-point Steiner prob - its virtual environment—the condition “shorter is better”—is lem appear in figure 3.