Genetics Problem Solving

Genetics Problem Solving-20
When trying to breed parents, the first idea that comes up is to take 50% from each one.However, in the Travelling Salesman Problem (TSP) it might lead to an invalid solution – in which each city will appear more than once. By taking the first part from the first parent, and then taking the rest of the cities according to their order of appearance on the second parent solution.Genetic algorithms imitate the evolution process in nature by evolving superior solutions to problems.

When trying to breed parents, the first idea that comes up is to take 50% from each one.However, in the Travelling Salesman Problem (TSP) it might lead to an invalid solution – in which each city will appear more than once. By taking the first part from the first parent, and then taking the rest of the cities according to their order of appearance on the second parent solution.

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In order to achieve this, we first need to evaluate each chromosome and give the better ones higher probabilities to produce children.

This is done by a function called the Fitness Function that receives a chromosome, and returns a score that represents how good the chromosome is in terms of the problem.

In order to understand how to do that, we’ll use the Traveling Salesman Problem (TSP): A solution to the problem can be represented as an ordered list of cities, when each one describes the desired route.

And it’s important to point out that every city should be listed exactly once.

What’s the connection between evolutionary algorithms and mother nature, and how can it help solve complicated computing problems?

Wikipedia defines evolution as “a change in the heritable characteristics of biological populations over successive generations”.

While it often relates to mother nature, animals or humans, it’s also a part of the computing world.

In the following post we’ll explore evolution through genetic algorithms, and see how it can help us solve problems faster.

And as we mentioned earlier, better chromosomes will get higher probabilities.

Or in other words, this is where we use natural selection and now it’s time to move to the process of evolution.

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