Massively parallel non-linear computation

We start with a two CA proliferon, CA-0 (stem process) and CA-1 (sentinel). Initially the  set point of the delivery function is high, CA- does not deliver resources and is isolated.  When CA-0 reaches state=43, it sets the set point to p[1] – 0.1  and  CA-1 delivers its resources into the environment.  The meaning of the delivery function is explained in the previous chapter . The experiment investigates the effect of the CA-0 state at which delivery is activated  on CA-1 tolerance.

As the CA-0 state at which change state occurs, rises, so does the mean daily tolerance accumulation. Its maximum is at CA-0 state = 43. It is determined by two parameters: delivery activation state and change state. Both are 43, which is summarized by a couplet {43,43} Max[tolerance[1]] ={43,43}. In summary, CA-1 starts as an isolated process. At CA-0 state = 43 it is activated and starts delivering its product into the environment. Finally the function change state starts its cycling. At {43,43} its tolerance accumulation rate is maximal. Tolerance stands for resources.

CA-0 cycles through 46 states. This study investigates how delivery activation and change state affect CA-1 tolerance accumulation rate. In other words,   how does Tolerance[delivery activation, change state] behave? The above experiment was repeated 46 times. The delivery of each CA-1 was activated at rising CA-0 states. After activation the change state was applied at different CA-0 states. We get 46 * 46 couplets From {0,0} till {46,46}.   This set is represented by the following tolerance accumulation surface:

The surface depicts tolerance rates of 46 * 46 solutions. The central plain is occupied by isolated CA in which delivery was not activated and change state was not effective. Even in their isolated state they accumulate tolerance at varying rates. Below this plain tolerance accumulation rate declines. (v. Tolerance accumulation. We may now ask what combination of CA states accumulates tolerance fastest? 

Eleven  out of 46 CA exhibited a tolerance rate  maximum. In the rest, tolerance was constant, yet varied between CA. The set of maxima is : {1,43},{8,14}, {10,8}, {14,8}, {18,28},{22,18},{26,10}, {31,10},{35,33},{39,30},{43,42}}.  The first number is the CA-0 state initiating delivery activation. The second is the CA-0 state initiating  state change. CA-tolerances of this set were summed up and added to the tolerances (at any coordinate) of the other CA. The overall mean daily tolerance gain above the isolated state (= 9.79) was 14.60 units. When the tolerances were summed up along the consecutive CA-0 exchange states the overall mean daily tolerance gain was 4.47.

Streaming processes

This surface may be generated in parallel. The stem CA-0 plants 46 zygotes of transient processes, When they mature it activates its two functions, delivery activation and change state, and the set starts delivering resources into the environment. In each CA cells are born and die.  The  change state function determines the CA period of 46, and when it starts. The surface depicts the average daily tolerance accumulation rate during steady state (homeorhesis).  It depicts a rate! The overall tolerance of the CA system  continually rises.  

Additional reading: Streaming organism

Massively parallel non-linear  computation

The surface depicts a parallel computation, and the question is how to chose the couplets so as to maximize tolerance gain rate by the CA set. We are dealing with different combinations of  couplets and ask whose tolerance accumulation rate is the fastest?   The solution was given above. 

The future objective is to let the 47 CA proliferon find the answer by itself. In other words let it evolve towards its most optimal solution. At this stage of my research  the proliferon exhibits one WOB property,  it always settles at a solution. The aim here is to  equip it with another WOB property, the capability (knowledge) to optimize. The objective here is to find a strategy which will guide the proliferon to settle at the most optimal solution with the fastest tolerance accumulation rate.