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.
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
Models like support vector machines or Gaussian process regression often require positive semi-definite kernels. These kernels may be based on distance functions. While definiteness is proven for common distances and ...
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The effort devoted to hand-crafting neural network image classifiers has motivated the use of architecture search to discover them automatically. Although evolutionary algorithms have been repeatedly applied to neural...
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Learning classifier systems are adaptive learning systems which have been widely applied in a multitude of application domains. However, there are still some generalization problems unsolved. The hurdle is that fitnes...
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In multi-objective Bayesian optimization and surrogate-based evolutionary algorithms, Expected HyperVolume Improvement (EHVI) is widely used as the acquisition function to guide the search approaching the Pareto front...
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