In this paper, we consider a capacitated multiple allocation hub location problem derived from a practical application in network design of German wagonload traffic. Due to the difficulty to solve even small data sets...
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In this paper, we consider a capacitated multiple allocation hub location problem derived from a practical application in network design of German wagonload traffic. Due to the difficulty to solve even small data sets to optimality, we present two matheuristics: a local search matheuristic and an extension of an evolutionary algorithm matheuristic. Computational results are presented to demonstrate and compare the efficiency of both approaches for real-sized instances.
An algorithm to perform mate selection in aquaculture breeding using a computational optimization procedure called "differential evolution" (DE) was applied under optimum contribution selection and mate sele...
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An algorithm to perform mate selection in aquaculture breeding using a computational optimization procedure called "differential evolution" (DE) was applied under optimum contribution selection and mate selection scenarios, to assess its efficiency in maximizing the genetic merit while controlling inbreeding. Real aquaculture data sets with 8,782 Nile tilapias from five generations and 79,144 coho salmon from eight generations were used to optimize objective functions accounting for coancestry of parents and expected genetic merit and inbreeding of the future progeny. The mate selection results were compared with those from the realized scenario (real mates), truncation selection and optimum contribution selection method. Mate selection allowed reducing inbreeding up to 73% for Nile tilapia, compared with truncation selection, and up to 20% for coho salmon, compared with realized scenario. There was evidence that mate selection outperformed optimum contribution selection followed by minimum inbreeding mating in controlling inbreeding under the same expected genetic gain. The developed algorithm was computationally efficient in maximizing the objective functions and flexible for practical application in aquaculture breeding.
Association rule mining is one of the most important data mining tasks. It corresponds to the determination of rules that associate items to other items in a data set, where the items are attributes in transactional d...
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ISBN:
(纸本)9783642353796;9783642353802
Association rule mining is one of the most important data mining tasks. It corresponds to the determination of rules that associate items to other items in a data set, where the items are attributes in transactional databases. Although evolutionary algorithms have been used in this task for some time, there are few applications of immune algorithms to such problem. This paper presents one typical genetic algorithm plus two clonal selection algorithms applied to association rule mining under the perspective of several measures of interest.
Striking an effective balance between exploration and exploitation (E&E) is still one of the major concerns when using evolutionary algorithms (EAs) in dynamic environments. In this work, a new scheme for adaptive...
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ISBN:
(纸本)9781450349390
Striking an effective balance between exploration and exploitation (E&E) is still one of the major concerns when using evolutionary algorithms (EAs) in dynamic environments. In this work, a new scheme for adaptively balancing E&E in EAs is proposed. Based on the results of a statistical Pre-Post analysis of the population, the next search mode can be decided (i.e., exploration or exploitation). The experimental results showed that our proposal excels versus several competing approaches from the state of the art.
The significance of bio-inspired evolutionary algorithms has attracted many applications for obtaining best solutions to their optimisation problems in the past decades. This paper is about the application of one of t...
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The significance of bio-inspired evolutionary algorithms has attracted many applications for obtaining best solutions to their optimisation problems in the past decades. This paper is about the application of one of these algorithms, namely, quantum particle swarm optimisation algorithm for parameter extraction of solar photovoltaic cells using current-voltage (I-V) characteristics. This algorithm has been used here to extract five parameters, namely, photocurrent, saturation current, series resistance, shunt resistance and ideality factor that influence the I-V relationship of single diode model solar photovoltaic cells. This approach has been validated for a cell and a module. Simulations using Matlab software have shown that the simulated I-V characteristics obtained using the extracted parameters have good agreement with the experimental I-V values. The reason for the interest taken in undertaking this work is to suggest a good and an accurate simulator for solar system designers.
This paper introduces Gnowee, a modular, Python-based, open-source hybrid metaheuristic optimization algorithm (Available from https://***/SlaybaughLab/Gnowee). Gnowee is designed for rapid convergence to nearly globa...
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We present a new method for finding video CNN architectures that capture rich spatio-temporal information in videos. Previous work, taking advantage of 3D convolutions, obtained promising results by manually designing...
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—Quality diversity is a recent family of evolutionary search algorithms which focus on finding several well-performing (quality) yet different (diversity) solutions with the aim to maintain an appropriate balance bet...
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Large-scale global optimization (LSGO) is known as one of the most challenging problem for evolutionary algorithms (EA). The most advanced algorithms for LSGO are based on cooperative coevolution with problem decompos...
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ISBN:
(数字)9781538694688
ISBN:
(纸本)9781538694695
Large-scale global optimization (LSGO) is known as one of the most challenging problem for evolutionary algorithms (EA). The most advanced algorithms for LSGO are based on cooperative coevolution with problem decomposition using grouping methods. In our previous studies, we have proposed a novel random adaptive grouping algorithm (RAG) that combines the ideas of random dynamic grouping and learning dynamic grouping. We have demonstrated that an approach based on the DECC and the RAG outperforms some state-of-the-art LSGO algorithms on the IEE CEC LSGO benchmarks. In this study, we have investigated the problem of tuning group sizes within the decomposition stage in details. We have evaluated the performance of the DECC-RAG algorithm with LSGO 2010 and 2013 benchmarks. The results of numerical experiments are presented and discussed. The results demonstrates how the performance of the RAG depends on the group sizing for each type of LSGO problems.
In computer security, guidance is slim on how to prioritize or configure the many available defensive measures, when guidance is available at all. We show how a competitive co-evolutionary algorithm framework can iden...
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ISBN:
(纸本)9781450357647
In computer security, guidance is slim on how to prioritize or configure the many available defensive measures, when guidance is available at all. We show how a competitive co-evolutionary algorithm framework can identify defensive configurations that are effective against a range of attackers. We consider network segmentation, a widely recommended defensive strategy, deployed against the threat of serial network security attacks that delay the mission of the network's operator. We employ a simulation model to investigate the effectiveness over time of different defensive strategies against different attack strategies. For a set of four network topologies, we generate strong availability attack patterns that were not identified a priori. Then, by combining the simulation with a co-evolutionary algorithm to explore the adversaries' action spaces, we identify effective configurations that minimize mission delay when facing the attacks. The novel application of co-evolutionary computation to enterprise network security represents a step toward course-of-action determination that is robust to responses by intelligent adversaries.1
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