# Evolution dispute

For what's probably the first time in my life, I find myself incapable of thinking of a decent response, probably because it's been an extremely long time since I've been in a remotely challenging debate, or learned anything about evolution. I thought I'd just ask for any suggestions in what to say.

(Discussing the first single-celled organisms, and how they evolved through mutation)

"The bacteria were struggling for survival. So this means as their DNA were mutating, the DNA had to get it right. It's like dices, you have to get all 6 out of 1,000 of dices to survive. Since the cells were struggling, this implies that they're not going to live for long. Therefore it also implies that the cells had to get 6 out of 1,000 right as soon as possible﻿ process in the DNA, which would be impossible﻿ since DNA is just a molecule that doesn't think."

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### Replies to This Discussion

HA! funny you should mention that quarter thing. I did that in biology class this past saturday. I got 8 tails in a row LOL and there were at least 20 people in my class so imagine how many times that actually happened! Right on Nelson! : )

In addition to what Nelson says you also have the process of mutation, recombination and selection as in this example

### Designing the Car

Each car is a set of 8 randomly chosen vectors: direction and magnitude. All the vectors radiate from a central point (0,0) and are connected with triangles.

For each wheel it randomly chooses a vertex to put the axle on, and picks an axle angle from 0 to 2 pi. If it chooses -1 for the vertex that wheel is turned off. There is nothing to prevent multiple wheels from being on the same vertex.

### The Chromosome: Representing the Data

The design of the chromosome is probably the most important step in making a successful genetic algorithm. Each car represents one chromosome, and its layed out like this:

 CartAngle0 CartMag0 CartAngle1 CartMag1 ... CartAngle7 CartMag7 WheelVertex0 AxleAngle0 WheelRadius0 WheelVertex1 AxleAngle1 WheelRadius1

Therefore each chromosome/car has 16 + 3 * 2 = 22 variables, each represented as a real number (or integer) with varying ranges.

In version 2, I've extended the chromosome to add 3 more variables for each wheel (vertex, axle angle, and radius) for a total of 8 possible wheels. This increases the number of variables to 16 + 3 * 8 = 40. However when the user decreases the maximum number of wheels, the chromosome size also decreases to reduce the variables.

### Selection

At the end of each generation, pairs of parents have to be selected to produce offspring for the next generation. That's the selection process and I've implemented two algorithms.

#### Roulette-Wheel Selection

This is the most obvious selection strategy, since it chooses parents based on their fitness scores. Specifically, it finds the sum of all fitness scores for that generation and divides each score by the sum to get the probability. Summing the probabilities creates a wheel we can select from. Here's an example with a population size of 4:

This screenshot was taken right at the beginning of the 2nd generation (gen 1). the 4 cars got scores of 3.9, 0.2, 6, and 1. Using these it calculates the roulette wheel probabilities:

 score probability roulette wheel piece 3.9 35.1% 0 to 35.1 0.2 1.8% 35.2 to 36.9 6.0 54.1% 37 to 91 1.0 9.0% 91 to 100

Then by picking a random number from 0-100 it can immediately see where it falls on the roulette wheel and pick that car for mating. Removing the selected car from the process, it repeats to get another car to mate. This process continues n/2 times where n is the population size. In this case n=4 so 2 pairs mate.

You can see the importance of the target score in this example, which stopped one of the cars at 6, to keep the scores in the same range. Even then, that car has a 54.1% chance of being picked each round of selection.

To prevent the algorithm from converging too fast theres a chance it won't use the roulette wheel and just randomly select a mate. For version 1 this was as high as 40%, otherwise the small population didn't lost diversity quickly and it would fall into local optimum.

#### Tournament Selection

Selection pressure can more be controlled more easily with tournament selection, which really helps keep high scoring individuals from sweeping the population too early. I've modified the concept to make sure every car is the small population gets a chance in the tournament. Here's the idea:

`*for each car A``    *pick a random car B (excluding A)    *highest score of A and B wins tournament    *put winner in mating pool*randomly pick pairs from mating pool for crossover`

This is deterministic tournament selection since it always selects the one with the higher score, and with the smallest possible tournament size of 2, the selection pressure is kept as small as possible.

#### Voting

Tournament selection is only implemented in version 2 and it easily allows the addition of user voting. If a car has an upvote it wins the tournament, regardless of its score. If both cars have an upvote the scores decide the winner, same as if neither has an upvote. Downvotes immediately remove that car from the mating pool!

### Crossover

 Parents A B Children AB BA

Here are the associated chromosomes for the cars shown above:

Car Angle0 Mag0 Angle1 Mag1 ... ... ... ... ... ... ... ... ... ... ... ... WheelVertex0 AxleAngle0 WheelRadius0 WheelVertex1 AxleAngle1 WheelRadius1
A 0.769 2.614 0.584 0.319 0.278 2.883 0.666 1.13 0.305 2.752 0.376 2.507 0.814 1.963 0.392 2.872 3 5.284 0.434 7 2.625 1.191
B 0.535 2.682 0.732 2.256 0.422 0.149 0.676 0.578 0.709 2.774 0.592 2.623 0.519 1.531 0.924 0.404 -1 0.704 0.122 4 0.167 0.409
AB 0.535 2.682 0.584 0.319 0.278 2.883 0.666 1.13 0.305 2.752 0.376 2.507 0.814 1.963 0.392 2.872 3 5.284 0.434 7 2.625 0.409
BA 0.769 2.614 0.732 2.256 0.422 0.149 0.676 0.578 0.709 2.774 0.592 2.623 0.519 1.531 0.924 0.404 -1 0.704 0.122 4 0.167 1.191

The two cars A and B crossover to produce parents AB and BA. I'm using two point crossover, which means a two random points along the chromosome are selected and everything in between is swapped (as indicated by the colors above). In this case the 3rd position and and the 2nd to last postion are chosen. Most notably, this takes the big wheel from car A and gives it to AB, while AB gets the small wheel. Cars are chosen by their scores but theres no guarantee that two high scoring cars will produce high scoring offspring.

### Mutation

In addition to crossover, each generation the chromosomes go through mutation. This means theres a probability that each aspect of the car (or variable in the chromosome) will change, as determined by the mutation rate slider set by the user. When a variable mutates, a new value is randomly chosen in the desired range. A new random color is also chosen to visually illustrate the mutation. By definition at 100% mutation rate, every variable is chosen randomly each generation and no information is retained.

Here's an example using car AB from above:

Car Angle0 Mag0 ... ... ... ... ... ... ... ... Angle5 Mag5 ... ... ... ... WheelVertex0 AxleAngle0 WheelRadius0 WheelVertex1 AxleAngle1 WheelRadius1
AB 0.535 2.682 0.584 0.319 0.278 2.883 0.666 1.13 0.305 2.752 0.376 2.507 0.814 1.963 0.392 2.872 3 5.284 0.434 7 2.625 0.409
ABm 0.535 2.682 0.584 0.319 0.278 2.883 0.666 1.13 0.305 2.752 0.376 0.940 0.814 1.963 0.392 2.872 4 5.284 0.434 7 2.625 0.409

In this case two variables have mutated, one of the magnitudes describing the car (blue), and the position of one of the wheels (orange). Mutation is performed after crossover for each child.

#### Torque and Spring Tension

The tension on shock springs and the torque of the wheels is determined automatically by the weight of the car. These values were chosen to produce a fun simulation. The torque is different for each wheel: torque = mass * gravity * sin(maxClimbAngle) / radius.

I read a while ago(couple of months) that it was probably RNA (precursor to DNA) which was formed first and the very first life forms mutated so many times per reproduction that the majority of their offspring were useless. But they also managed to reproduce so many times that some of their offspring was viable and in turn reproduced some more.....eventually the mutation rates slowed down and became several different species of RNA organism.

bit off topic but relevant I think...anyway the point is that yes, these life forms did need to get all 6's out of 1000 (or more) dice rolls to establish themselves but this was possible for them because of their rates of mutation and rates of reproduction.

Look around for the research if you need it....not sure where I read it

It has already been mentioned that RNA would have been at play long before DNA entered the picture.  Secondly the first 'organisms' wouldn't have even been cells, but would have been replicating reactions of amino acids (abundantly available in the universe under the right conditions) suspended in an interim medium (currently theorized to be clay).  So the initial process wasn't even life as we would define it, but simply a complex chemical reaction that resulted in trillions of proto-cells sloughing off of the medium into a primordial soup where 99.999% of them would have disintegrated immediately.  Of the few thousand that survived, they continued to replicate with 99.999% of imperfect replications disintegrating.  Even so, the incredibly high reproduction rate of such simple proto-cells would have allowed small populations to flourish here and there for short bursts.  Looks like one of them eventually took root.

I'm not really up to speed on where abiogenesis is at these days, but my description would suit a late 90's filler animation on Saturday afternoon between a couple of educational children's shows.

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