Search results for "memetic"

showing 10 items of 47 documents

A penalty-based edge assembly memetic algorithm for the vehicle routing problem with time windows

2010

In this paper, we present an effective memetic algorithm for the vehicle routing problem with time windows (VRPTW). The paper builds upon an existing edge assembly crossover (EAX) developed for the capacitated VRP. The adjustments of the EAX operator and the introduction of a novel penalty function to eliminate violations of the time window constraint as well as the capacity constraint from offspring solutions generated by the EAX operator have proven essential to the heuristic's performance. Experimental results on Solomon's and Gehring and Homberger benchmarks demonstrate that our algorithm outperforms previous approaches and is able to improve 184 best-known solutions out of 356 instance…

Mathematical optimizationSDG 16 - PeaceGeneral Computer ScienceHeuristic (computer science)EconomicsSDG 16 - Peace Justice and Strong InstitutionsCrossoverMemetic algorithmManagement Science and Operations ResearchEAX mode/dk/atira/pure/sustainabledevelopmentgoals/peace_justice_and_strong_institutionsPenalty functionVehicle routingJustice and Strong InstitutionsModeling and SimulationVehicle routing problemMemetic algorithmPenalty methodEnhanced Data Rates for GSM EvolutionRouting (electronic design automation)AlgorithmTime windowsMathematicsComputers and Operations Research
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Differential Evolution with Scale Factor Local Search for Large Scale Problems

2010

This chapter proposes the integration of fitness diversity adaptation techniques within the parameter setting of Differential Evolution (DE). The scale factor and crossover rate are encoded within each genotype and self-adaptively updated during the evolution by means of a probabilistic criterion which takes into account the diversity properties of the entire population. The population size is also adaptively controlled by means of a novel technique based on a measurement of the fitness diversity. An extensive experimental setup has been implemented by including multivariate problems and hard to solve fitness landscapes. A comparison of the performance has been conducted by considering a st…

Mathematical optimizationScale (ratio)Computer sciencebusiness.industryRobustness (computer science)Differential evolutionMemetic algorithmLocal search (optimization)Scale factorbusinessMetaheuristicEvolutionary computation
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A Memetic Algorithm for Binary Image Reconstruction

2008

This paper deals with a memetic algorithm for the reconstruction of binary images, by using their projections along four directions. The algorithm generates by network flows a set of initial images according to two of the input projections and lets them evolve toward a solution that can be optimal or close to the optimum. Switch and compactness operators improve the quality of the reconstructed images which belong to a given generation, while the selection of the best image addresses the evolution to an optimal output.

Mathematical optimizationSettore INF/01 - InformaticaQuadratic assignment problemBinary imageComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONMemetic algorithmtomografy reconstructionFlow networkImage (mathematics)Set (abstract data type)Compact spaceMemetic algorithmAlgorithmSelection (genetic algorithm)Mathematics
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Fitness diversity based adaptation in Multimeme Algorithms:A comparative study

2007

This paper compares three different fitness diversity adaptations in multimeme algorithms (MmAs). These diversity indexes have been integrated within a MmA present in literature, namely fast adaptive memetic algorithm. Numerical results show that it is not possible to establish a superiority of one of these adaptive schemes over the others and choice of a proper adaptation must be made by considering features of the problem under study. More specifically, one of these adaptations outperforms the others in the presence of plateaus or limited range of variability in fitness values, another adaptation is more proper for landscapes having distant and strong basins of attraction, the third one, …

Mathematical optimizationbusiness.industryMachine learningcomputer.software_genreEvolutionary computationRange (mathematics)SpiteMemetic algorithmArtificial intelligenceAdaptationbusinesscomputerAlgorithmMathematicsDiversity (business)2007 IEEE Congress on Evolutionary Computation
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A hybrid genetic algorithm with local search: I. Discrete variables: optimisation of complementary mobile phases

2001

Abstract A hybrid genetic algorithm was developed for a combinatorial optimisation problem. The assayed hybridation modifies the reproduction pattern of the genetic algorithm through the application of a local search method, which enhances each individual in each generation. The method is applied to the optimisation of the mobile phase composition in liquid chromatography, using two or more mobile phases of complementary behaviour. Each of these phases concerns the optimal separation of certain compounds in the analysed mixture, while the others can remain overlapped. This optimisation approach may be useful in situations where full resolution with a single mobile phase is unfeasible. The o…

Mathematical optimizationbusiness.industryProcess Chemistry and TechnologyComputationBinary numberResolution (logic)Computer Science ApplicationsAnalytical ChemistryEncoding (memory)Genetic algorithmMemetic algorithmCombinatorial searchLocal search (optimization)businessAlgorithmSpectroscopySoftwareMathematicsChemometrics and Intelligent Laboratory Systems
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A precise measurement of the Z resonance parameters through its hadronic decays

1990

A measurement of the cross section for e+e-→ hadrons using 11 000 hadronic decays of the Z boson at ten different center-of-mass energies is presented. A three-parameter fit gives the following values for the Z mass MZ, the total width ΓZ, the product of the electronic and hadronic partial widths ΓeΓh, and the unfolded pole cross section σ0: MZ = 91.171 ± 0.030 (stat.) ± 0.030 (beam) GeV, ΓZ = 2.511 ± 0.065 GeV, ΓeΓh = 0.148 ± 0.006 (stat.) ± 0.004 (syst.) GeV2, σ0 = 41.6 ± 0.7 (stat.) ± 1.1 (syst.) nb, Good agreement with the predictions of the standard model is observed. From a two-parameter fit the number of massless neutrino generations is found to be Nv = 2.91 ± 0.26. Thus the hypothes…

Nuclear and High Energy PhysicsParticle physics[PHYS.HEXP] Physics [physics]/High Energy Physics - Experiment [hep-ex]pragmatismtruthElectron–positron annihilationHadronfictionpossible worlds01 natural sciencesResonance (particle physics)webStandard ModelRDFNuclear physicsCross section (physics)semantic webesthetics0103 physical sciencesmemetic[PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex]010306 general physicsNuclear Experimentmetalanguagemodal logicPhysicsInternet010308 nuclear & particles physicstrust3. Good healthMassless particlePhysique des particules élémentairesHigh Energy Physics::ExperimentFísica nuclearNeutrinoBeam (structure)Particle Physics - Experiment
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Integrating Cross-Dominance Adaptation in Multi-objective Memetic Algorithms

2008

This chapter proposes a novel adaptive memetic approach for solving multi-objective optimization problems. The proposed approach introduces the novel concept of crossdominance and employs this concept within a novel probabilistic scheme which makes use of the Wigner distribution for performing coordination of the local search. Thus, two local searchers are integrated within an evolutionary framework which resorts to an evolutionary algorithm previously proposed in literature for solving multi-objective problems. These two local searchers are a multi-objective version of simulated annealing and a novel multi-objective implementation of the Rosenbrock algorithm.

Optimization problembusiness.industryComputer scienceSimulated annealingEvolutionary algorithmProbabilistic logicWigner distribution functionMemetic algorithmLocal search (optimization)Artificial intelligencebusinessMulti-objective optimization
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An enhanced memetic differential evolution in filter design for defect detection in paper production.

2008

This article proposes an Enhanced Memetic Differential Evolution (EMDE) for designing digital filters which aim at detecting defects of the paper produced during an industrial process. Defect detection is handled by means of two Gabor filters and their design is performed by the EMDE. The EMDE is a novel adaptive evolutionary algorithm which combines the powerful explorative features of Differential Evolution with the exploitative features of three local search algorithms employing different pivot rules and neighborhood generating functions. These local search algorithms are the Hooke Jeeves Algorithm, a Stochastic Local Search, and Simulated Annealing. The local search algorithms are adap…

PaperQuality ControlMathematical optimizationPopulationEvolutionary algorithmmultimeme algorithmsdigital filter designArtificial IntelligenceImage Interpretation Computer-AssistedFIR filterHumansIndustryLocal search (optimization)Computer Simulationmemetic algorithmseducationMetaheuristicMathematicsProbabilityedge detectioneducation.field_of_studyElectronic Data ProcessingStochastic ProcessesModels Statisticalbusiness.industrydifferential evolutionpaper productionModels TheoreticalComputational MathematicsFilter designDifferential evolutionSimulated annealingMemetic algorithmbusinessAlgorithmsSoftware
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Differential Evolution with Fitness Diversity Self-adaptation

2009

This chapter proposes the integration of fitness diversity adaptation techniques within the parameter setting of Differential Evolution (DE). The scale factor and crossover rate are encoded within each genotype and self-adaptively updated during the evolution by means of a probabilistic criterion which takes into account the diversity properties of the entire population. The population size is also adaptively controlled by means of a novel technique based on a measurement of the fitness diversity. An extensive experimental setup has been implemented by including multivariate problems and hard to solve fitness landscapes. A comparison of the performance has been conducted by considering both…

Scale factor (computer science)Mathematical optimizationComputer scienceFitness landscapeDifferential evolutionPopulation sizeProbabilistic logicMemetic algorithmAdaptation (computer science)Evolutionary computation
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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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