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Selection – Selecting pairs of parent solutions according to their Fitness value 3.Crossover – Breeding the selected parents to produce an offspring 4.According to Charles Darwin, also known as the “father” of evolutionary thought, natural selection is the key force that helps preserve good changes, and eradicate bad ones. Genetic algorithms mimic the power of evolution with code, along with natural selection, in order to solve problems better and faster.
We can be as creative as we want, as long as we make sure that we’re generating a valid solution that combines both parents.
Before we can take the child we created into the “next generation”, we need to apply mutation with a very low probability. Well, it depends on the problem we’re facing and just like before, we need to make sure that the mutated chromosome is still a valid solution.
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.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.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.N can be in the range of 10’s to 100’s, according to our memory limit. Now that we know how genetic algorithms work, it’s time to wonder when we should actually use them.Then, just like the circle of life, we will calculate a fitness function for each child, give it selection probabilities, run the evolution, get a new generation and so on. The top use case is for optimization problems, when we’re facing a big search space, where other search algorithms have failed.In this example, our solution population consists of a collection of lists of cities.To form our first generation, we will randomly generate a collection of solutions, and we need to make sure that each generated solution is valid (and is a possible solution to the problem). The next step will be taking these chromosomes into a process of evolution, and use the power of natural selection to obtain a better population.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.