Intelligent Reasoning

Promoting, advancing and defending Intelligent Design via data, logic and Intelligent Reasoning and exposing the alleged theory of evolution as the nonsense it is. I also educate evotards about ID and the alleged theory of evolution one tard at a time and sometimes in groups

Wednesday, November 23, 2011

Genetic/ Evolutionary Algorithms and My Front-Loaded Evolution

An article on Talk Origins states:
In a broadly general sense, GAs do have a goal: namely, to find an acceptable solution to a given problem. In this same sense, evolution also has a goal: to produce organisms that are better adapted to their environment and thus experience greater reproductive success. But just as evolution is a process without specific goals, GAs do not specify at the outset how a given problem should be solved. The fitness function is merely set up to evaluate how well a candidate solution performs, without specifying any particular way it should work and without passing judgment on whatever way it does invent. The solution itself then emerges through a process of mutation and selection.

Forget that evolution does not have the goal stated- the point is that GAs are an example of front-loading- that is they start with everything they need to solve some problem. Front-loading does NOT require that the solution be known nor that the specific process to finding the solution be known.

What is required is the specification of what you need- what are you trying to solve.

For example a GA was used to design an antenna. The engineers did not know what the antenna would look like. But what they had were the specifications the antenna needed to meet- again from Talk Origins:
Altshuler and Linden 1997 used a genetic algorithm to evolve wire antennas with pre-specified properties. The authors note that the design of such antennas is an imprecise process, starting with the desired properties and then determining the antenna's shape through "guesses.... intuition, experience, approximate equations or empirical studies" (p.50). This technique is time-consuming, often does not produce optimal results, and tends to work well only for relatively simple, symmetric designs. By contrast, in the genetic algorithm approach, the engineer specifies the antenna's electromagnetic properties, and the GA automatically synthesizes a matching configuration.

THAT is front-loaded evolution.

So with my idea of front-loaded evolution we would have the initial conditions, the required resources, the specified result (ie what you are trying to accomplish) and then the algorithms to make it all happen.

Sadly evotards will never grasp any of that.


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