# Problem Solving In Genetics

When trying to breed parents, the first idea that comes up is to take 50% from each one.

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.In this example, our solution population consists of a collection of lists of cities.

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Once we have the population, we can move on to the evolution process, which consists of the following steps:1.

Fitness – Giving a score to each solution, that represent how good it is 2.

Mutation – Mutating the offspring solution with a very low probability The evolution process here leads to finding a “superior” solution to the problem, or at least so we hope.

When choosing to use genetic algorithms (that’s part of evolutionary algorithms), the first thing we need to understand is how to represent an individual solution in our population.

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

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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.

Genetic algorithms imitate the evolution process in nature by evolving superior solutions to problems.

Natural selection plays a major part in this process – the differential survival and reproduction of individuals due to differences in phenotype.

But, why would we think that genetic algorithms would perform better?

When thinking about it, we just start with a random collection of solutions hoping to eventually find a good one. Mutation – that encourages diversity which spreads the chromosomes over the search space, helping us discover as many hills as possible 2.

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