Constructive+evolution

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=Constructive evolution=

In our assessment of the role of space and the effects of interlocking evolutionary and ecological dynamics, our main approach has been to take classical population dynamic models and augment them with space and ongoing phenotypic mutations. In this way we have included local interactions and have allowed for mesoscale patterns to arise. Moreover, ongoing mutations contrast with the more classical approach of changing parameters. In this setting we have studied the persistence and invasion dynamics of different phenotypes and thus optimization of parameters in the system. In this section we deviate from this approach and take a different perspective, namely one focusing on **constructive evolution**. In the 70's, [|Maynard Smith] emphasized the fact that although we know about fitness and mutations, and thus natural selection, that doesn't tell us about **how** evolution constructs **new** things. Maynard Smith therefore suggested that every evolutionary biologist should go to the zoo once a year and stand in front of an elephant and say:
 * "****//[|Elephant], I believe you came about by random mutations!"//**

In the following our goal is therefore to try and find a way to extend our modeling in order to allow for //constructive// processes to occur by mutation and selection. To guide us in this enterprise we make use of several useful observations:
 * the number of [|genotypes] is **much greater** than the number of individuals. Individuals are therefore always a subset of genotype space (vs quasi-species equations where all mutants are present).
 * there is a complex genotype-phenotype mapping. This mapping is not very direct (without which you don't get nice [|Mendelian inheritance]), but should be very important, and mutations occur on genotypes but selection occurs on phenotypes.

[|Genetic algorithms]
Such an approach requires the view of Darwinian selection as an efficient design, or optimization, tool. To this end, [|John Holland] (1975) came up with genetic algorithms, or evolutionary computation, which incorporate: and //very importantly// (In fact Holland (1975) was one of the first to really think about the importance of such processes.)
 * a population of (coded) structures, or solutions, or cases
 * some fitness criterion
 * replication, or reproduction
 * decay, or death
 * mutational operators: not just point mutations but also duplications, deletions, rearrangements.

Such genetic algorithms are used for various systems with complex counterintuitive behaviour in order to design an optimized system such as: (Note: even nanotechnology requires evolutionary optimization, since [|intelligent design] apparently doesn't work! Therefore it is even more paradoxical for nanotechnology expert Cees Dekker to be such a proponent of intelligent design!)
 * [|computer networks], job selection
 * [|robotic control], body design
 * [|nanotechnology]
 * in vitro evolution of [|ribozymes] (cf RNA world)

Genetic algorithms therefore lead to a shift in methodology. Before we were concerned with changing parameters and saw that according to reproduction rates the direction of evolution could change. The focus was on **what evolves**. Now we set an external fitness criterion (goal) and things get fitness when they are closer to that goal. In this way we allow the system to discover how to achieve that goal. The focus is on **how things evolve**. With this shift in methodology we first study RNA evolution.

Next: RNA evolution