Creationism in Crisis - How Evolution Evolved Intelligently Without a Creator

Intelligent design without a creator? Why evolution may be smarter than we thought

The evolution of evolvability is a fascinating aspect of the Theory of Evolution. For example, the evolution of sexual reproduction seems counter-intuitive because it involves two copies of each allele, with the original often dominating, so any advantage from a beneficial mutation can be lost because it isn't expressed in the phenotype, while a deleterious mutation can be shielded from selection by a dominant allele.

However, because it also involves cross-over between chromosomes it can give rise to new combinations of alleles allowing evolution to 'experiment', with different evolutionary lines coming together to produce synergy where the total is greater than the sum of the parts, so sexual reproduction almost certainly evolved because it gave greater evolvability, i.e. it gave the organisms greater resilience in a changing environment.

Evolution is a process, not an event, and, as such, it can be improved (evolved) to maximise efficiency. Indeed, the mechanism of random change being tested in a selective environment makes that evolution inevitable. In the same way that machine learning is the process which underpins artificial intelligence, so evolution is the process by which species respond to environmental changes. Both processes can be improved by a natural evolutionary process, without the need for supernatural intervention.

In the following article from 2016, reprinted from The Conversation under a Creative Commons license, Professor Richard A. Watson, Associate Professor, Institute for Life Sciences/Electronics and Computer Science, University of Southampton, UK, explains how evolution could be evolving with an evolved natural intelligence which enables it to solve problems without the ability to look ahead the way an intelligent designer would work.

The article has been reformatted for stylistic consistence. The original can be read here:

Intelligent design without a creator? Why evolution may be smarter than we thought

Published: January 28, 2016 11.55am CET

According to creationists, the eyes of the great horned owl cannot be explained by Darwinian evolution.

Rachlin Susan, U.S. Fish and Wildlife Service

Richard A. Watson, University of Southampton

Charles Darwin’s theory of evolution offers an explanation for why biological organisms seem so well designed to live on our planet. This process is typically described as “unintelligent” – based on random variations with no direction. But despite its success, some oppose this theory because they don’t believe living things can evolve in increments. Something as complex as the eye of an animal, they argue, must be the product of an intelligent creator.

I don’t think invoking a supernatural creator can ever be a scientifically useful explanation. But what about intelligence that isn’t supernatural? Our new results, based on computer modelling, link evolutionary processes to the principles of learning and intelligent problem solving – without involving any higher powers. This suggests that, although evolution may have started off blind, with a couple of billion years of experience it has got smarter.

What is intelligence?

Intelligence can be many things, but sometimes it’s nothing more than looking at a problem from the right angle. Finding an intelligent solution can be just about recognising that something you assumed to be a constant might be variable (like the orientation of the paper in the image below). It can also be about approaching a problem with the right building blocks.

With good building blocks (for example triangles) it’s easy to find a combination of steps (folds) that solves the problem by incremental improvement (each fold covers more picture). But with bad building blocks (folds that create long thin rectangles) a complete solution is impossible.

Looking at a problem from the right angle makes it easy.

author

In humans, the ability to approach a problem with an appropriate set of building blocks comes from experience – because we learn. But until now we have believed that evolution by natural selection can’t learn; it simply plods on, banging away relentlessly with the same random-variation “hammer”, incrementally accumulating changes when they happen to be beneficial.

The evolution of evolvability

In computer science we use algorithms, such as those modelling neural networks in the brain, to understand how learning works. Learning isn’t intrinsically mysterious; we can get machines to do it with step by step algorithms. Such machine learning algorithms are a well-understood part of artificial intelligence. In a neural network, learning involves adjusting the connections between neurons (stronger or weaker) in the direction that maximises rewards. With simple methods like this it is possible to get neural networks to not just solve problems, but to get better at solving problems over time.

But what about evolution, can it get better at evolving over time? The idea is known as the evolution of evolvability. Evolvability, simply the ability to evolve, depends on appropriate variation, selection and heredity – Darwin’s cornerstones. Interestingly, all of these components can be altered by past evolution, meaning past evolution can change the way that future evolution operates.

For example, random genetic variation can make a limb of an animal longer or shorter, but it can also change whether forelimbs and hindlimbs change independently or in a correlated manner. Such changes alter the building blocks available to future evolution. If past selection has shaped these building blocks well, it can make solving new problems look easy – easy enough to solve with incremental improvement. For example, if limb lengths have evolved to change independently, evolving increased height will require multiple changes (affecting each limb) and intermediate stages may be worse off. But if changes are correlated, individual changes might be beneficial.

The idea of the evolution of evolvability has been around for some time, but the detailed application of learning theory is beginning to give this area a much needed theoretical foundation.

Gene networks evolve like neural networks learn.

author

Our work shows that the evolution of regulatory connections between genes, which govern how genes are expressed in our cells, has the same learning capabilities as neural networks. In other words, gene networks evolve like neural networks learn. While connections in neural networks change in the direction that maximises rewards, natural selection changes genetic connections in the direction that increases fitness. The ability to learn is not itself something that needs to be designed – it is an inevitable product of random variation and selection when acting on connections.

The exciting implication of this is that evolution can evolve to get better at evolving in exactly the same way that a neural network can learn to be a better problem solver with experience. The intelligent bit is not explicit “thinking ahead” (or anything else un-Darwinian); it is the evolution of connections that allow it to solve new problems without looking ahead.

So, when an evolutionary task we guessed would be difficult (such as producing the eye) turns out to be possible with incremental improvement, instead of concluding that dumb evolution was sufficient after all, we might recognise that evolution was very smart to have found building blocks that make the problem look so easy.

Interestingly, Alfred Russel Wallace (who suggested a theory of natural selection at the same time as Darwin) later used the term “intelligent evolution” to argue for divine intervention in the trajectory of evolutionary processes. If the formal link between learning and evolution continues to expand, the same term could become used to imply the opposite.
Richard A. Watson, Associate Professor, Institute for Life Sciences/Electronics and Computer Science, University of Southampton

Published by The Conversation.
Open access. (CC BY 4.0)

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