Alberto Ochoa-Rodríguez

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In this paper the optimization of additively decomposed discrete functions is investigated. For these functions genetic algorithms have exhibited a poor performance. First the schema theory of genetic algorithms is reformulated in probability theory terms. A schema deenes the structure of a marginal distribution. Then the conceptual algorithm BEDA is(More)
In the literature of Evolutionary Computation, it is very strange to find papers where the results of Evolutionary Algorithms are compared to other algorithms. Stochastic Hill Climbing is a simple optimization algorithm that has shown a competitive performance with respect to many powerful algorithms in the solution of different problems. It has also(More)
The goal of this research is to analyze how individual learning interacts with an evolutionary algorithm in its search for best candidates for the Busy Beaver problem. To study this interaction two learning models, implemented as local search procedures, are proposed. Experimental results show that, in highly irregular and prone to premature convergence(More)
In the last 30 years, a lot of effort has been dedicated to develop robust optimization methods like Evolutionary Algorithms, Simulated Annealing, and recently the so-called Estimation Distribution Algorithms. All these algorithms have to evaluate an objective functions many times. This situation leads us to the problem of reducing the cost and number of(More)
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