To supplement my previous post. This is something I found on the evolution of complexity:

The Discover piece is a popularized version of the Nature article at the bottom

Carl Zimmer. 2005.”Testing Darwin” Discover (February):28-35 + letters Discover (April): 8

p. 30 “One thing the digital organisms do particularly well is evolve. Avida is not a simulation of evolution; it is an instance of it,” Pennock says, “All the core parts of the Darwinian are there. These things replicate, they mutate, they are competing with one another. The very process of natural selection is happening there. If that’s central to the definition of life, then thee things count.”.. . Computer programs and DNA are both sets of instructions. Computer programs tell a computer how to process information, while DNA instructs a cell how to assemble proteins.
.. .
p. 32. The researchers set up an experiment to document how one particularly complex operation evolved. The operation, known as equals, consists of comparing pairs of binary numbers, bit by bit, and recording whether each pair of digits is the same. It’s a standard operation fond in software, but it’s not a simple one. The shortest equals program Ofria could write is 19 lines long. The chances that random mutations alone could produce it are about one in a thousand trillion trillion. To test Darwin’s idea that complex systems evolve from simple precursors, the Avida team set up rewards for simpler precursors and bigger rewards for more complex ones. The researchers set up an experiment in which the organisms replicate for 16,000 generations. They then repeated the experiment 50 times.
Avida beat the odds. In 23 of the 50 trials, evolution produced organisms that cold carry out the equals operation. And when the researchers took away rewards for simpler operations, the organisms never evolved an equals program. “when we looked at the 23 tests, they were all done in completely different ways,” adds Ofria. He was reminded of how Darwin pointed out that many evolutionary paths can produce the same complex organ. A fly and an octopus can both produce an image with their eyes, but their eyes are dramatically different from ours.. .
When the Avida team published their first results on the evolution of complexity in 2003, they were inundated with e-mails from creationists. Their work hit a nerve in the antievolution movement and hit it hard. A popular claim of creationists is that life shows signs of intelligent design, especially in its complexity. They argue that complex things could have never evolved because they don’t work unless all their parts are in place. But as Adami points out, if creationists were right, then Avida wouldn’t be able to produce complex digital organisms. A digital organism may use 19 or more simple routines in order to carry out the equals operation. If you delete any of the routines, it can’t do the job. “What we show is that there are irreducibly complex things and they can evolve,” says Adami.

p.32. Ecologists have found out that the more energy a habitat can provide organisms, the more species it can support.
But a habitat can get too productive. Then it supports fewer species. This pattern has emerged time and time again in studies on ecosystems ranging from grassland to Artic tundra. Until recently, a typical Avida experimental would end up with a single dominant organism. The Avida researchers suspected that was the result of providing an endless supply of food—in this case, numbers. Perhaps, they reasoned, if they put their digital organisms on a diet, they might evolve into different forms—just as it happens in nature. So the Avida team retooled their software to limit the supply of numbers flowing into their digital worlds. Then they made the numbers even more scarce by splitting them up into smaller supplies, each of which could be used only for a particular operation, such as adding two numbers. As the organisms used the numbers at a faster rate, they got a smaller benefit. And if too many organisms gorged themselves on a supply of numbers, they would stop replicating altogether. The Avida team subsequently flooded some digital worlds with numbers and limited others to a scant supply, and the same pattern of diversity found in global ecosystems emerged. When the number supply was low, only one type of organisms could survive. At intermediate levels, three or four different types emerged and coexisted. Each type evolved into a specialist at one or a few kinds of operations. But when the number supply got too abundant, diversity dropped to a single species again.
Bringing diversity into Avida has brought more bad news for those who think complexity cannot evolve. Ofria decided to run the complexity experiment over again, this time with a limit on the supply of numbers. “It just floored me,’ he says. “I went back and checked this so many ways.” In the original experiments, the organisms evolved the equals routine in 23 out of 50 trials. But when the experiment was run with a limited supply of numbers, all the trials produced organisms that could carry out the equals routine. What’s more, they needed only a fifth of the time to do it. Ofria suspects that the difference comes from the fact that several species are now evolving in the experiment rather than just one. More species mean more opportunities for success.

Richard E. Lenski, et al. 2003. “The Evolutionary Origin of Complex Features,” Nature 423 (8 May); 139-144

A long-standing challenge to evolutionary theory has been whether it can explain the origin of complex organismal features. We examined this issue using digital organisms—computer programs that self-replicate, mutate, compete and evolve. Populations of digital organisms often evolved the ability to perform complex logic functions requiring the coordinated execution of many genomic instructions. Complex functions evolved by building on simpler functions that had evolved earlier, provided that these were also selectively favoured. However, no particular intermediate stage was essential for evolving complex functions. The first genotypes able to perform complex functions differed from their non-performing parents by only one or two mutations, but differed from the ancestor by many mutations that were also crucial to the new functions. In some cases, mutations that were deleterious when they appeared served as stepping-stones in the evolution of complex features. These findings show how complex functions can originate by random mutation and natural selection.

Bernard