Titlebook: EVOLVE- A Bridge between Probability, Set Oriented Numerics and Evolutionary Computation; Emilia Tantar,Alexandru-Adrian Tantar,Oliver Sch

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A Comparative Study of Heuristic Conversion Algorithms, Genetic Programming and Return Predictabilitdapt the buying and selling rules of the investigated algorithms resulting in a new algorithm. Results show that a genetic programming approach does not lead to good new algorithms. We extend former works by using the . as a measure of risk, and by applying competitive analysis.

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eal-valued MOPs. We show the main aspects to be considered when building local search operators using the objective function gradients, and when coupling them with evolutionary algorithms. We present an overview of our current methods with discussion about their convenience for particular kinds of problems.

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Book 2013or creating a bridge between probability, set-oriented numerics and evolutionary computation. .The volume encloses a collection of contributions that were presented at the EVOLVE 2011 international workshop, held in Luxembourg, May 25-27, 2011, coming from invited speakers and also from selected reg

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https://doi.org/10.1007/978-3-7091-4832-7re for selecting the most important dependencies in EDAs by truncating regular vines. Moreover, this chapter also shows how the use of mutual information in the learning of graphical models implies a natural way of employing copula functions.

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ar Genetic Algorithm(cGA).We replace themutation operator by amutation based on concepts of PSO. We present two hybrid algorithms and analyze their performance using a set of different problems. The results obtained are quite satisfactory in efficacy and efficiency, outperforming in most cases existing algorithms for a set of problems.