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The bombastic notion of evolving logic programs has been taken up in earnest by John Koza, a professor of computer science at Stanford. Koza was one of John Holland's students who brought knowledge of Holland's genetic algorithms out of the dark ages of the '60s and '70s into the renaissance of parallelism of the late '80s.

Rather than merely explore the space of possible equations, as Sims the artist did, Koza wanted to breed the best equation to solve a particular problem. One could imagine (as a somewhat silly example) that in the space of possible pictures there might be one that would induce cows gazing at it to produce more milk. Koza's method can evolve the equations that would draw that particular picture. In this farfetched idea, Koza would keep rewarding the equations which drew a picture that even minutely increased milk production until there was no further increase. For his actual experiments, though, Koza choose more practical tests, such as finding an equation that could steer a moving robot.

But in a sense his searches were similar to those of Sims and the others. He hunted in the Borgian Library of possible computer programs -- not on an aimless mission to see what was there, but to find the best equation for a particular practical problem. Koza wrote in Genetic Programming, "I claim that the process of solving these problems can be reformulated as a search for a highly fit individual computer program in the space of possible computer programs."

For the same reason computer experts said Ray's scheme of computer evolution couldn't work, Koza's desire to "find" equations by breeding them bucked convention. Everyone "knew" that logic programs were brittle and unforgiving of the slightest alteration. In computer science theory, programs had two pure states: (1) flawlessly working; or (2) modified and bombed. The third state -- modified at random yet working -- was not in the cards. Slight modifications were known as bugs, and people paid a lot of money to keep them out. If progressive modification and improvement (evolution) of computer equations was at all possible, the experts thought, it must be so only in a few precious areas or specialized types of programs.

The surprise of artificial evolution has been that conventional wisdom was so wrong. Sims, Ray, and Koza have wonderful evidence that logical programs can evolve by progressive modifications.

Koza's method was based on the intuitive hunch that if two mathematical equations are somewhat effective in solving a problem, then some parts of them are valuable. And if the valuable parts from both are recombined into a new program, the result might be more effective than either parent. Koza randomly recombined, in thousands of combinations, parts of two parents, banking on the probabilistic likelihood that one of those random recombinations would include the optimal arrangement of valuable parts to better solve the problem.

There are many similarities between Koza's method and Sims's. Koza's soup, too, was a mixture of about a dozen arithmetical primitives, such as add, multiply, cosine, rendered in the computer language LISP. The units were strung together at random to form logical "trees," a hierarchical organization somewhat like a computer flow chart. Koza's system created 500 to 10,000 different individual logic trees as the breeding population. The soup usually converged upon a decent offspring in about 50 generations.

Variety was forced by sexually swapping branches from one tree to the next. Sometimes a long branch was grafted, other times a mere twig or terminal "leaf." Each branch could be thought of as an intact subroutine of logic made of smaller branches. In this way, bits of equation (a branch), or a little routine that worked and was valuable, had a chance of being preserved or even passed around.

All manner of squirrely problems can be solved by evolving equations. A typical riddle which Koza subjected to this cure was how to balance a broom on a skateboard. The skateboard must be moved back and forth by a motor to keep the inverted broom pivoted upright in the board's center. The motor-control calculations are horrendous, but not very different from the control circuits needed for maneuvering robot arms. Koza found he could evolve a program to achieve this control.

Other problems he tested evolutionary equations against included: strategies for navigating a maze; rules for solving quadratic equations; methods to optimize the shortest route connecting many cities (also known as traveling salesman problem); strategies for winning a simple game like tic-tac-toe. In each case, Koza's system sought a formula for the test problem rather than a specific answer for a specific instance of the test. The more varied instances a sound formula was tested against, the better the formula became with each generation.

While equation breeding yields solutions that work, they are usually the ugliest ones you could imagine. When Koza began to inspect the insides of his highly evolved prizes, he had the same shock that Sims and Ray did: the solutions were a mess! Evolution went the long way around. Or it burrowed through the problem by some circuitous loophole of logic. Evolution was chock-full of redundancy. It was inelegant. Rather than remove an erroneous section, evolution would just add a countercorrecting section, or reroute the main event around the bad sector. The final formula had the appearance of being some miraculous Rube Goldberg collection of items that by some happy accident worked. And that's exactly what it was, of course.

Take as an example a problem Koza once threw at his evolution machine. It was a graph of two intertwining spirals. A rough approximation would be the dual spirals in pinwheel. Koza's evolutionary equation machine had to evolve the best equation capable of determining on which of the two intertwined spiral lines each of about 200 data points lay.

Koza loaded his soup with 10,000 randomly generated computer formulas. He let them breed, as his machine selected the equations that came closest to getting the right formula. While Koza slept, the program trees swapped branches, occasionally birthing a program that worked better. He ran the machine while he was on vacation. When he returned, the system had evolved an answer that perfectly categorized the twin spirals.

This was the future of software programming! Define a problem and the machine will find a solution while the engineers play golf. But the solution Koza's machine found tells us a lot about the handiwork of evolution. Here's the equation it came up with:

(SIN (IFLTE (IFLTE (+ Y Y) (+ X Y) (- X Y) (+ Y Y)) (* X X) (SIN (IFLTE (% Y Y) (% (SIN (SIN (% Y 0.30400002))) X) (% Y 0.30400002) (IFLTE (IFLTE (% (SIN (% (% Y (+ X Y)) 0.30400002)) (+ X Y)) (% X 0.10399997) (- X Y) (* (+ -0.12499994 -0.15999997) (- X Y))) 0.30400002 (SIN (SIN (IFLTE (% (SIN (% (% Y 0.30400002) 0.30400002)) (+ X Y)) (% (SIN Y) Y) (SIN (SIN (SIN (% (SIN X) (+ -0.12499994 -0.15999997))))) (% (+ (+ X Y) (+ Y Y)) 0.30400002)))) (+ (+ X Y) (+ Y Y))))) (SIN (IFLTE (IFLTE Y (+ X Y) (- X Y) (+ Y Y)) (* X X) (SIN (IFLTE (% Y Y) (% (SIN (SIN (% Y 0.30400002))) X) (% Y 0.30400002) (SIN (SIN (IFLTE (IFLTE (SIN (% (SIN X) (+ -0.12499994 -0.15999997))) (% X -0.10399997) (- X Y) (+ X Y)) (SIN (% (SIN X) (+ -0.12499994 -0.15999997))) (SIN (SIN (% (SIN X) (+ -0.12499994 -0.15999997)))) (+ (+ X Y) (+ Y Y))))))) (% Y 0.30400002))))).

Not only is it ugly, it's incomprehensible. Even for a mathematician or computer programmer, this evolved formula is a tar baby in the briar patch. Tom Ray says evolution writes code that only an intoxicated human programmer would write, but it may be more accurate to say evolution generates code that only an alien would write; it is decidedly inhuman. Backtracking through the evolving ancestors of the equation, Koza eventually traced the manner in which the program tackled the problem. By sheer persistence and by hook and crook it found a laborious roundabout way to its own answer. But it worked.

The answer evolution discovered seems strange because almost any high school algebra student could write a very elegant equation in a single line that described the two spirals.

There was no evolutionary pressure in Koza's world toward simple solutions. His experiment could not have found that distilled equation because it wasn't structured to do so. Koza tried applying parsimony in other runs but found that parsimony added to the beginning of a run dampened the efficiency of the solutions. He'd find simple but mediocre to poor solutions. He has some evidence that adding parsimony at the end of evolutionary procedure -- that is, first let the system find a solution that kind of works and then start paring it down -- is a better way to evolve succinct equations.

But Koza passionately believes parsimony is highly overrated. It is, he says, a mere "human esthetic." Nature isn't particularly parsimonious. For instance, David Stork, then a scientist at Stanford, analyzed the neural circuits in the muscles of a crayfish tail. The network triggers a curious backflip when the crayfish wants to escape. To humans the circuit looks baroquely complex and could be simplified easily with the quick removal of a couple of superfluous loops. But the mess works. Nature does not simplify simply to be elegant.