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From CreationWiki, the encyclopedia of creation science

Genetic algorithms are search methods that use computer programming to find solutions to combinatorial optimization problems using methods inspired by biological evolution. Because they were inspired by the theory of Evolution, some evolutionists claim them as evidence that microbe to man evolution is possible.

Genetic algorithms start with an initial population, in which the "genes" are usually random. They then follow the same basic pattern:

1. The population is evaluated based on how well they solve the problem that the algorithm is designed to solve.
2. The best are selected to reproduce, often with a process similar to recombination.
3. The offspring are then mutated
4. The process restarts at #1 unless the program termination condition has been reached.

While some evolutionists claim genetic algorithms as evidence that microbe to man evolution is possible, the claims are flawed on several points.

• Genetic algorithms often start with a random "gene" sets. In the real world an organism with random genes would not live.
• Genetic algorithms have no fatal steps. In the real world genes are complex instructional codes such that combinations that are intermediaries between two viable states can often be fatal. It is much like a computer program, in that that has discrete commands, but trying to go from one command to another 1 bit at a time will cause the program to crash.
• Genetic algorithms place the instructions for critical functions such as reproduction beyond the influence of the mutations, as such no mutation will disrupt those functions. In the real world critical functions can often be destroyed by mutations.
• Genetic algorithms never produce new capabilities beyond what is preprogrammed into them. Microbe to man evolution requires totally new and complex capabilities to be developed many many times.
• Genetic algorithms start with fully functional processes designed into them. Microbe to man Evolution requires these processes to be developed from scratch, but they are needed for life.
• Genetic algorithms are designed by intelligent programmers with a specific problem in mind and fully functional from the start. To properly mimic biological evolution, the "organisms" would need:
  • The "organisms" would have to be a fully functional program, with a detailed programing language that tells it how to do every thing.
  • The "organisms" would have to develop the programing language from scratch with no input from a programmer.
  • The "organisms" would have to develop the entire operating system from scratch with no input from a programmer.
  • The "organisms" would have to develop a system to read and write the programing instructions also from scratch with no input from an intelligent agent.
  • The "organisms" would have to develop and build the computer memory and processor from scratch with no input from an intelligent agent.
• Genetic algorithms have a narrow definition of fitness. The "fitness" of the "organism" is measured based on how well is fits a specific problem. In the real world organisms ether live or die. If they live long enough, they usually reproduce. According to Wikipedia this is a type of problem that genetic algorithms (GAs) can not effectively solve.

GAs can not effectively solve problems in which there is no way to judge the fitness of an answer other than right/wrong, as there is no way to converge on the solution. These problems are often called "needle in a haystack" problems. Reference: Genetic algorithm - Wikipedia

In a given environment an organism has two answers by which its fitness for evolution is judged: live or die (right or wrong). Thus, real world organisms have no way to converge on a solution.

Furthermore General Evolution requires an increase genetic diversity from a single cell to the vast variety of life we observes in the world, but genetic algorithms start a maximum diversity and narrow it to a solution. This actually means that genetic algorithms go in the opposite of General Evolution theory.

While some evolutionists claim genetic algorithms as evidence that microbe to man Evolution is possible, it is clear that they do not adequately represent biology and as such show nothing about plausibility of microbe to man Evolution.

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One example touted by Evolutionists is an "artificial life" program called Avida. Despite the claims about this program, it does not come anywhere near showing the possibility of microbe to man Evolution. One flaw is that each bit of the "genome" makes up a complete command, and one that is actually encoded outside the genome; this does not fit the genomes of real organisms.

Unlike most genetic algorithm programs Avida does include two reproduction commands as part of its "genome" but they only tell the "organism" when to reproduce and what mode ( sexual or asexual ) to use. In both cases the actual instructions are outside the "genome" and are thus unaffected by mutation. This does allow for a mutation that renders an "organism" sterile, but no mutation changes the preprogrammed instructions inside each command.

These "artificial organisms" do not develop new abilities, that are not designed into the program, but simply rearrange existing abilities. Avida starts with a created kind of "organism" and only produces varieties of that organism, in perfect agreement with Creation science.

Related References

• Genetic algorithm
• GA Playground (Genetic Algorithms Toolkit)
• Genetic algorithms—do they show that evolution works?
• The Digital Life Lab at Caltech
• Avida, a Biologically Unrealistic Model for Neo-Darwinian Theory
• Evolution by Intelligent Design: A Response to Lenski et al.

See Also

• Genetics