New papers confirm: Sorry Mr. Dawkins - No free lunch today cont'd

Some people are bothered by the craziness that surrounds the Darwin-v-Design controversy, but I take a more relaxed view. Don’t get me wrong. If I thought there were nothing but craziness, I’d be as frustrated as anyone. But serious science is being done on both sides of the debate, and that should give us confidence that a truer picture of biology will become visible as the smoke clears.

Gauger continues:

In the recent past, several papers have been published that claim to demonstrate that biological evolution can readily produce new genetic information, using as their evidence the ability of various evolutionary algorithms to find a specific target. This is a rather large claim.

It has thus fallen to others in the scientific or engineering community to evaluate these published claims. How well do these algorithms model biology? How exactly was the work done? Do the results make sense? Are there unexamined variables that might affect the interpretation of results? Are there hidden sources of bias? Are the conclusions justified or do they go beyond the scope of what has been shown?

A new paper by Montañez et al. [1], just published in the journal BIO-Complexity, answers some of these questions for the evolutionary algorithm ev [2], one of the computer programs proposed to simulate biological evolution. As perhaps should be no surprise, the authors found that ev uses sources of active information (meaning information added to the search to improve its chances of success compared to a blind search) to help it find its target. Indeed, the algorithm is predisposed toward success because information about the search is built into its very structure.

These same authors have previously reported on the hidden sources of information that allowed another evolutionary algorithm, AVIDA [3-5], to find its target. Once again, active information introduced by the structure of the algorithm was what allowed it to be successful.
These results confirm that there is no free lunch for evolutionary algorithms. Active information is needed to guide any search that does better than a random walk.

Abstract of Douglas Axe’s paper:

To explain life's current level of complexity, we must first explain genetic innovation. Recognition of this fact has generated interest in the evolutionary feasibility of complex adaptations--adaptations requiring multiple mutations, with all intermediates being non-adaptive. Intuitively, one expects the waiting time for arrival and fixation of these adaptations to have exponential dependence on d, the number of specific base changes they require. Counter to this expectation, Lynch and Abegg have recently concluded that in the case of selectively neutral intermediates, the waiting time becomes independent of d as d becomes large. Here, I confirm the intuitive expectation by showing where the analysis of Lynch and Abegg erred and by developing new treatments of the two cases of complex adaptation--the case where intermediates are selectively maladaptive and the case where they are selectively neutral. In particular, I use an explicit model of a structured bacterial population, similar to the island model of Maruyama and Kimura, to examine the limits on complex adaptations during the evolution of paralogous genes--genes related by duplication of an ancestral gene. Although substantial functional innovation is thought to be possible within paralogous families, the tight limits on the value of d found here (d = 2 for the maladaptive case, and d = 6 for the neutral case) mean that the mutational jumps in this process cannot have been very large. Whether the functional divergence commonly attributed to paralogs is feasible within such tight limits is far from certain, judging by various experimental attempts to interconvert the functions of supposed paralogs. This study provides a mathematical framework for interpreting experiments of that kind, more of which will needed before the limits to functional divergence become clear.

A note about the journal BIO-Complexity:

BIO-Complexity BIO-Complexity is a peer-reviewed scientific journal with a unique goal. It aims to be the leading forum for testing the scientific merit of the claim that intelligent design (ID) is a credible explanation for life. Because questions having to do with the role and origin of information in living systems are at the heart of the scientific controversy over ID, these topics—viewed from all angles and perspectives—are central to the journal's scope.

To achieve its aim, BIO-Complexity is founded on the principle of critical exchange that makes science work. Specifically, the journal enlists editors and reviewers with scientific expertise in relevant fields who hold a wide range of views on the merit of ID, but who agree on the importance of science for resolving controversies of this kind. Our editors use expert peer review, guided by their own judgement, to decide whether submitted work merits consideration and critique. BIO-Complexity aims not merely to publish work that meets this standard, but also to provide expert critical commentary on it.

Apparently, there are no vacancies for trolls in the foreseeable future.

Find out why there is an intelligent design controversy: