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Nowadays hybrid evolutionary algorithms, i.e, heuristic search algorithms combining several mutation operators some of which are meant to implement stochastically a well known technique designed for the specific problem in question while some others playing the role of random search, have become rather popular for tackling various NP-hard optimization problems. While empirical studies demonstrate...
The performance of Evolutionary Programming (EP) is affected by many factors (e.g. mutation operators and selection strategies). Although the conventional approach with Gaussian mutation operator may be efficient, the initial scale of the whole population can be very large. This may lead to the conventional EP taking too long to reach convergence. To combat this problem, EP has been modified in various...
Vertex cover is one of the best known NP-hard combinatorial optimization problems. Experimental work has claimed that evolutionary algorithms (EAs) perform fairly well for the problem and can compete with problem-specific ones. A theoretical analysis that explains these empirical results is presented concerning the random local search algorithm and the (1+1)-EA. Since it is not expected that an algorithm...
Recently it has been proved that the (1+1)-EA produces poor worst-case approximations for the vertex cover problem. In this paper the result is extended to the (1+lambda)-EA by proving that, given a polynomial time, the algorithm can only find poor covers for an instance class of bipartite graphs. Although the generalisation of the result to the (mu+1)-EA is more difficult, hints are given in this...
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