0000000000013057

AUTHOR

Giuseppe Leonardo Cascella

0000-0003-1471-9799

showing 2 related works from this author

An adaptive multimeme algorithm for designing HIV multidrug therapies.

2007

This paper proposes a period representation for modeling the multidrug HIV therapies and an Adaptive Multimeme Algorithm (AMmA) for designing the optimal therapy. The period representation offers benefits in terms of flexibility and reduction in dimensionality compared to the binary representation. The AMmA is a memetic algorithm which employs a list of three local searchers adaptively activated by an evolutionary framework. These local searchers, having different features according to the exploration logic and the pivot rule, have the role of exploring the decision space from different and complementary perspectives and, thus, assisting the standard evolutionary operators in the optimizati…

ScheduleMathematical optimizationComputer scienceAnti-HIV AgentsHIV therapy designAdaptive algorithms; HIV therapy design; Memetic algorithms; Nonlinear integer programming; Algorithms; Anti-HIV Agents; Biomimetics; Computer Simulation; Drug Combinations; Drug Design; Drug Therapy Computer-Assisted; HIV Infections; Humans; Immunity Innate; Models ImmunologicalHIV InfectionsReduction (complexity)Computer-AssistedDrug TherapyModelsBiomimeticsGeneticsInnateHumansComputer SimulationRepresentation (mathematics)MetaheuristicStatistical hypothesis testingFlexibility (engineering)Applied MathematicsNonlinear integer programmingImmunityModels ImmunologicalAdaptive algorithmsImmunity InnateDrug Therapy Computer-AssistedDrug CombinationsImmunologicalDrug DesignMemetic algorithmsMemetic algorithmAlgorithmAlgorithmsBiotechnologyPremature convergenceIEEE/ACM transactions on computational biology and bioinformatics
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An adaptive prudent-daring evolutionary algorithm for noise handling in on-line PMSM drive design

2007

This paper studies the problem of the optimal control design of permanent magnet synchronous motor (PMSM) drives taking into account the noise due to sensors and measurement devices. The problem is analyzed by means of an experimental approach which considers noisy data returned by the real plant (on-line). In other words, each fitness evaluation does not come from a computer but from a real laboratory experiment. In order to perform the optimization notwithstanding presence of the noise, this paper proposes an Adaptive Prudent- Daring Evolutionary Algorithm (APDEA). The APDEA is an evolutionary algorithm with a dynamic parameter setting. Furthermore, the APDEA employs a dynamic penalty ter…

NoiseControl theoryComputer scienceEvolutionary algorithmOptimal controlEvolutionary computationSelection (genetic algorithm)2007 IEEE Congress on Evolutionary Computation
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