Why Is Evolution Hard To Understand?

A friend of mine, pondering on the Darwin Day Celebration asks why evolution is so much misunderstood compared to, say Einstein's relativity theory, which is generally acknowledged to be hard to understand.

Jacques Monod once said (as quoted by Dawkins), "Anther curious aspect of the theory of evolution is that, everybody thinks he understands it". This is true, but many thinks evolution only in its much simplified form "Our great great grandparents were apes".
Compared this to the almost-zero platitude "Everything is relative".
Both sentences are intuitive, easy to understand and very misleading.

When it comes to the harder parts of Einstein's tensor manipulations and differential geometry, we rather leave them to experts to verify, and take them for granted.
It is different with evolution theory, for the reasons I am outlining below.

There are similarities between evolution theory and relativity theory, but they also have very significant differences.

Relativity is rather remote from us, we can visualize space travel like in the twins paradox, but only in our imagination. Evolution, on the other hand, concerns us very much, we are in the middle of it. Inevitably, our cultural baggage from our upbringing interferes. Some of us consider themselves created with a soul and a self, and find it difficult to accept evolution, not for lack of understanding, but because we don't want to understand.

Another difficulty is the time span involved in evolution. We can watch evolution in our life times in cases like the evolution of anti-biotic resistance, and some evolution in animals and plants, but the really big changes are beyond our life times.

Relativity is mostly described in the language of Mathematics. Evolution, as a biological process is much more complex than physical sciences.
Mathematics is also useful in biology, but there are many biological processes which cannot be described succinctly using Mathematics. Instead one uses algorithms to describe its processes. Unfortunately, the algorithms are also very complicated, much more so than computer programs, and some of so-called genetic algorithms are not faithful representations of biological evolution (more of this below), as they make too many simplifying assumptions.

Some people have difficulties understanding the difference between a random algorithm and a non-deterministic algorithm. Evolution is non-deterministic and it employs random variables as its components (e.g. mutations), but evolution is not a random process. Non-deterministic search algorithms (e.g. Tabu search, simulated annealing, ant colony algorithm) for example, employ random elements, but they can, under certain conditions, be guaranteed to find an optimal solution. I.e. a non-deterministic algorithm may have a deterministic outcome.

Evolution is technically defined as a change in the frequencies of the genes found in natural populations.
Evolution is not about the origin of life or the origin of the universe.

The characteristics of evolution is that it must have a copying operator (replication, reproduction, inheritance) which makes imperfect copies giving rise to variations, which together with other types of variations form a population from which selection is made by one method or the other.

Natural selection is the most important selection mechanism, but it is not the only one. Discoveries of other selection methods (including artificial selection) do not invalidate evolution. Natural selection exists side by side with other selection mechanisms.

Fitness is often used as a measure for selection. This is an unrealistic simplification, reinforced by the concepts of the genetic algorithm family. Genetic algorithm can be useful, for example in solving optimization problems, where there is a fitness function defined externally.
In biological evolution, it is more likely that there are multiple fitness functions, and the fitness measures do not have to be defined a priori.

I think this is a very crucial aspect of evolution: fitness is adaption to the environment. In the beginning the environment is considered as external and fixed or beyond our control, but as we evolve, we also change the environment, we change the definition of fitness. Evolution does not occur in isolation, as in idealistic genetic algorithms, the species co-evolved together and with their environment.
This is an example of a strange loop or what Barendregt called reflection. It is a very potent recursion in Logic, Mathematics, in Lambda Calculus, in mindfulness, in nature and in life.
This is the second strange loop in biological evolution. The first is the loop gene -> host ->gene by reproduction. The third strange loop is gene -> host -> gene by genetic engineering and cloning.

It should be noted that variation can arise from many sources: mutation, migration, genetic drift, recombination, etc. It was recently reported that viruses could form genetic material from which to build new genes.

Cloning and genetic engineering would bring new mechanisms for replication and variation, enriching and not invalidating the framework of the evolution.

It is true that the detail algorithm of biological is not complete, and it is not clear whether it ever will, but it does not make evolution theory less valid.
By analogy, Newton's gravitation theory remained valid, discoveries of electricity-magnetic forces and other forces do not change its validity. Only general relativity makes corrections to Newton's theory, in the sense of limiting its domain of validity.
Again, should we have complete theories of black holes and black matter, they would become refinements of our existing physical theories.
Advances in molecular biology (e.g. miRNA) will most likely refine or augment evolution theory.

I have alluded several times above the misconceptions arising by using the model of genetic algorithms. To be fair, they were inspired by, and not meant as models for biological evolution. They are useful in themselves. They can help, to some degree, in visualizing natural evolution, as in the interplay of mutation and crossover rates, how it sometimes lead to blind alleys, and how building blocks are formed.
As models of natural evolution, they are too crude, and misleading.

I think it would be very useful to have a new algorithmic model of evolution, which has a high degree of faithfulness to biological evolution. Such an algorithm could be used to demonstrate how complexity can emerged from simple things. This would save us from unproductive discussions about irreducible complexity put forward by creationists.

In summary, we still have a long way to go to make evolution understood by "the man on the street". There are many misunderstandings about evolution. Darwin Celebration Day should be celebrated by our efforts to communicate evolution. It is hoped that there will be less people who do not understand evolution.

“I think everyone is realizing that we need to be doing a great deal more. We just haven't made the effort to communicate evolution to people in terms they can understand. Evolution is exciting.”
Judy Diamond