Commit c585b7b8 authored by Markus Klinik's avatar Markus Klinik
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criticism of EAs

parent 6b9118c1
......@@ -60,3 +60,17 @@ In order to do this, the algorithm has to select chromosomes from the old popula
In general it is a bad idea to just select the best chromosomes of a population, as this can lead to fast convergence towards a local optimum.
Populations should be diverse and include non-optimal solutions from other places in the search space, in order to discover good solutions far away from each other.
To preserve diversity, the algorithm should select a mix of good and bad chromosomes to produce offspring.
\subsection{Criticism of Evolutionary Algorithms}
By their nature, probabilistic algorithms can never guarantee convergence to the best solution, if it exists.
Articles about evolutionary algorithms usually use a number of computer experiments to obtain performance data which is then compared to estimated best solutions.
In the literature on algorithms, it is customary to provide correctness proofs for algorithms.
No such proof can be given for probabilistic algorithms.
The best we can do is guarantee that populations do not get worse over time, by always keeping the best solution found so far.
Another problem with evolutionary algorithms is that their effectiveness depends on the input parameters, like population size and mutation probability.
Which parameters work well can change with the problem instance, and may require some adjustments until satisfying results can be reliably produced.
End users, who are only interested in the results and not the inner workings of the algorithm should not be bothered with such technicalities.
We need further research and computer experiments with realistic scenarios to develop a parameter set that performs reasonably reliable in practice for our problem.
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