Tuesday, February 17, 2009

Genetic Alogorithms provide an example of the practical benefits of understanding evolution

Creationists often say that teaching evolution in schools isn’t necessary because there are no practical things that can be learned from evolution. In fact some of them say that learning evolution is actually bad!

Here’s the opinion of the Institute for Creation Research[1]:

"Evolution, at best, may have been thought of as a benign assumption in the scientific world, but in recent years we have come to realize that frequently the basic presuppositions of evolutionary thinking have actually been a malignancy retarding true scientific discovery and preventing biological research from efficiently meeting the needs of mankind in many areas. Creation with plan and purpose, on the other hand, provides an excellent basis for solving problems in the real world of scientific research. So it seems that only of the best things scientists could do for their research and for the needy world would be to develop a foundational attitude like that of the great 17th-century physical astronomer, John Kepler, who said that he was merely 'thinking God's thoughts after Him.'"

It is worth discussing this in some detail.

Genetic Algorithms (GA) (also called “Evolutionary Systems”) are an example where an understanding of evolutionary processes has proven to be very beneficial.

As I’ve described elsewhere, there are three things that are needed for evolution to take place:

1. Some form of reproduction
2. Random changes to the “design” that gets reproduced
3. A mechanism that “selects” some changes over others.

Dogs evolve. They reproduce and they get random genetic “changes”. The selection mechanism, however, is not nature. Instead it is based on decisions made by dog breeders.

That’s evolution – change over time.

In nature the selection mechanism is the differential reproductive success (or lack of success) of various genetic changes in the population of organisms.

Some people have studied those requirements and decided to implement them in computers allowing the computers to do the design.

GA is a very active field of study. If I performed a Google search on “Genetic Algorithms” (with the quote marks) I got an estimated 1,510,000 hits[2].

Here’s how Wikipedia describes them[3]:

"Genetic algorithms are implemented as a computer simulation in which a population of abstract representations (called chromosomes or the genotype of the genome) of candidate solutions (called individuals, creatures, or phenotypes) to an optimization problem evolves toward better solutions. Traditionally, solutions are represented in binary as strings of 0s and 1s, but other encodings are also possible. The evolution usually starts from a population of randomly generated individuals and happens in generations. In each generation, the fitness of every individual in the population is evaluated, multiple individuals are stochastically selected from the current population (based on their fitness), and modified (recombined and possibly randomly mutated) to form a new population. The new population is then used in the next iteration of the algorithm. Commonly, the algorithm terminates when either a maximum number of generations has been produced, or a satisfactory fitness level has been reached for the population. If the algorithm has terminated due to a maximum number of generations, a satisfactory solution may or may not have been reached.

"Genetic algorithms find application in bioinformatics, phylogenetics[4], computational science, engineering, economics, chemistry, manufacturing, mathematics, physics and other fields."

This is very much like the process that drives biological evolution. The GA field has been the source of a great deal of study including a number of PhD dissertations[5].

The most interesting example of GA is, I believe, this one[6]:

"Sometimes, evolutionary systems come up with designs that are quite similar to ones humans came up with -- an evolutionary circuit design system at Stanford University, on its own, developed a layout that was nearly identical to early (1950's-era) human-designed circuits.

"Sometimes, however, evolutionary systems come up with surprises. A researcher at the University of Sussex in Brighton, England, used a set of chips called a 'field programmable gate array', which can be reprogrammed on the fly, and actually had the circuits evolve (rather than evolving the design in a software simulation). His goal was relatively simple: have the chips be able to recognize two distinct sounds. But the result stunned researchers. Not only did the eventual design use fewer than one-half the circuits that a human-engineered layout used, it did so in a way that took the researcher months to unravel. The evolutionary process had used analog elements (such as heat buildup and circuit pathways) of otherwise digital chips to accomplish the task."

Creationists look at this example and claim that it has nothing to do with biological evolution. A typical comment from a creationist when presented with the example of GA is:

> "Genetic algorithm" is a design, intellect involved.
> Without that the whole idea would not function at all.

It is surely true that GA engineers and programmers set up and “intelligently design” the GA system. But they do not control the results from the GA programs, as this previous field programmable logic array example demonstrates. Often they are surprised by the results.

In essence, what we see is something that is exactly analogous to Theistic Evolution. Theistic Evolution is the belief that God created the rules needed for the process of evolution, but then God let that evolutionary process follow its own course without further (or possibly minimal) intervention. That is what the GA engineers and programmers have done.

Once a creationist accepts Theistic Evolution, then they aren’t really a creationist any more.

[1] http://www.icr.org/article/252/, referenced on December 1, 2008
[2] Google search performed on December 1, 2008
[3] http://en.wikipedia.org/wiki/Genetic_algorithm, referenced on December 1, 2008
[4] Phylogenetics is the study of evolutionary relationships among organisms, usually at or above the species level.
[5] A Google search performed on December 1, 2008 for “Genetic Algorithm” along with “dissertation” yielded over 57,000 hits.
[6] http://openthefuture.com/writing/PCR/evolve.htm, referenced on December 1, 2008

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