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Genetic Algorithms

Genetic Algorithms

Genetic algorithms use the principles found in the evolutionary process to create formulas that can solve problems. Like mutation, small parts of the formulas are changed and then the formula is evaluated to see if the change has improved it.


Search Space

If we are solving some problem, we are usually looking for some solution, which will be the best among others. The space of all feasible solutions (it means objects among those the desired solution is) is called search space (also state space). Each point in the search space represent one feasible solution. Each feasible solution can be “marked” by its value or fitness for the problem. We are looking for our solution, which is one point (or more) among feasible solutions – that is one point in the search space.

The looking for a solution is then equal to a looking for some extreme (minimum or maximum) in the search space. The search space can be whole known by the time of solving a problem, but usually we know only a few points from it and we are generating other points as the process of finding solution continues.

The problem is that the search can be very complicated. One does not know where to look for the solution and where to start. There are many methods, how to find some suitable solution (ie. not necessarily the best solution), for example hill climbing, tabu search, simulated annealing and genetic algorithm. The solution found by this methods is often considered as a good solution, because it is not often possible to prove what is the real optimum.

Evolutionary algorithms now surpass human designers – [newscientist.com]

EAs take two parent designs – for a boat hull, say – and blend components of each, perhaps taking the surface area of one and the curvature of another, to produce multiple hull offspring that combine the features of the parents in different ways. Then the algorithm selects those offspring it considers are worth re-breeding – in this case those with the right combination of parameters to make a better hull. The EA then repeats the process. Although many offspring will be discarded, after thousands of generations or more, useful features accumulate in the same design, and get combined in ways that likely would not have occurred to a human designer. This is because a human does not have the time to combine all the possibilities for each feature and evaluate them, but an EA does.

Evolutionary Computation

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