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Gnowee: A hybrid metaheuristic optimization algorithm for constrained, black box, combinatorial mixed-integer design

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arXiv 2018年

作者： Bevins, James E. Slaybaugh, R.N. Air Force Institute of Technology Department of Engineering Physics 2950 Hobson Way Wright-Patterson Air Force BaseOH45433 United States University of California Berkeley Nuclear Engineering Department 4173 Etcheverry Hall BerkeleyCA94720 United States

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 globally optimum solutions for complex, constrained nuclear engineering problems with mixed-integer and combinatorial design vectors and high-cost, noisy, discontinuous, black box objective function evaluations. Gnowee's hybrid metaheuristic framework is a new combination of a set of diverse, robust heuristics that appropriately balance diversification and intensification strategies across a wide range of optimization problems. This novel algorithm was specifically developed to optimize complex nuclear design problems;the motivating research problem was the design of material stack-ups to modify neutron energy spectra to specific targeted spectra for applications in nuclear medicine, technical nuclear forensics, nuclear physics, etc. However, there are a wider range of potential applications for this algorithm both within the nuclear community and beyond. To demonstrate Gnowee's behavior for a variety of problem types, comparisons between Gnowee and several well-established metaheuristic algorithms are made for a set of eighteen continuous, mixedinteger, and combinatorial benchmarks. These results demonstrate Gnoweee to have superior exibility and convergence characteristics over a wide range of design spaces. We anticipate this wide range of applicability will make this algorithm desirable for many complex engineering applications. Copyright © 2018, The Authors. All rights reserved.

关键词： evolutionary algorithms

Evolving Space-Time Neural Architectures for Videos

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arXiv 2018年

作者： Piergiovanni, A.J. Angelova, Anelia Toshev, Alexander Ryoo, Michael S. Google Brain

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 video CNN architectures. We here develop a novel evolutionary search algorithm that automatically explores models with different types and combinations of layers to jointly learn interactions between spatial and temporal aspects of video representations. We demonstrate the generality of this algorithm by applying it to two meta-architectures, obtaining new architectures superior to manually designed architectures. Further, we propose a new component, the iTGM layer, which more efficiently utilizes its parameters to allow learning of space-time interactions over longer time horizons. The iTGM layer is often preferred by the evolutionary algorithm and allows building cost-efficient networks. The proposed approach discovers new and diverse video architectures that were previously unknown. More importantly they are both more accurate and faster than prior models, and outperform the state-of-the-art results on multiple datasets we test, including HMDB, Kinetics, and Moments in Time. We will open source the code and models, to encourage future model development 1. Copyright © 2018, The Authors. All rights reserved.

关键词： evolutionary algorithms

Quality Diversity Through Surprise

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arXiv 2018年

作者： Gravina, Daniele Liapis, Antonios Yannakakis, Georgios N. Institute of Digital Games University of Malta Msida2080 Malta

—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 between divergence and convergence during search. While quality diversity has already delivered promising results in complex problems, the capacity of divergent search variants for quality diversity remains largely unexplored. Inspired by the notion of surprise as an effective driver of divergent search and its orthogonal nature to novelty this paper investigates the impact of the former to quality diversity performance. For that purpose we introduce three new quality diversity algorithms which employ surprise as a diversity measure, either on its own or combined with novelty, and compare their performance against novelty search with local competition, the state of the art quality diversity algorithm. The algorithms are tested in a robot navigation task across 60 highly deceptive mazes. Our findings suggest that allowing surprise and novelty to operate synergistically for divergence and in combination with local competition leads to quality diversity algorithms of significantly higher efficiency, speed and robustness. Copyright © 2018, The Authors. All rights reserved.

关键词： evolutionary algorithms

On tuning group sizes in the Random Adaptive Grouping Algorithm for Large-scale Global Optimization Problems

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On tuning group sizes in the Random Adaptive Grouping Algori...

International Conference on Applied Mathematics & Computational Science

作者： Evgenii Sopov Alexey Vakhnin Eugene Semenkin Department of system analysis and operations research Reshetnev Siberian State University of Science and Technology

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.

关键词： Large-scale global optimization Variable grouping method Cooperative coevolution evolutionary algorithms evolutionary algorithm Rag algorithms Global optimization tuning Random BENCHMARKS

Regularized evolution for image classifier architecture search

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arXiv 2018年

作者： Real, Esteban Aggarwal, Alok Huang, Yanping Le, Quoc V. Google Brain Mountain ViewCA United States

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 network topologies, the image classifiers thus discovered have remained inferior to human-crafted ones. Here, we evolve an image classifier-AmoebaNet-A-that surpasses hand-designs for the first time. To do this, we modify the tournament selection evolutionary algorithm by introducing an age property to favor the younger genotypes. Matching size, AmoebaNet-A has comparable accuracy to current state-of-the-art ImageNet models discovered with more complex architecture-search methods. Scaled to larger size, AmoebaNet-A sets a new state-of-the-art 83.9% top-1 / 96.6% top-5 ImageNet accuracy. In a controlled comparison against a well known reinforcement learning algorithm, we give evidence that evolution can obtain results faster with the same hardware, especially at the earlier stages of the search. This is relevant when fewer compute resources are available. Evolution is, thus, a simple method to effectively discover high-quality architectures. Copyright © 2018, The Authors. All rights reserved.

关键词： evolutionary algorithms

Adversarial co-evolution of attack and defense in a segmented computer network environment 18

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Adversarial co-evolution of attack and defense in a segmente...

Proceedings of the Genetic and evolutionary Computation Conference Companion

作者： Erik Hemberg Joseph R. Zipkin Richard W. Skowyra Neal Wagner Una-May O'Reilly MIT Cambridge Massachusetts MIT Lincoln Laboratory Lexington Massachusetts

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

关键词： cybersecurity evolutionary algorithms co-evolution network

An empirical approach for probing the definiteness of kernels

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arXiv 2018年

作者： Zaefferer, Martin Bartz-Beielstein, Thomas Rudolph, Günter Faculty of Computer Science and Engineering Science TH Köln University of Applied Sciences Steinmüllerallee 1 Gummersbach51643 Germany Department of Computer Science TU Dortmund University Otto-Hahn-Str. 14 Dortmund44227 Germany

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 kernels, a proof for a new kernel may require too much time and effort for users who simply aim at practical usage. Furthermore, designing definite distances or kernels may be equally intricate. Finally, models can be enabled to use indefinite kernels. This may deteriorate the accuracy or computational cost of the model. Hence, an efficient method to determine definiteness is required. We propose an empirical approach. We show that sampling as well as optimization with an evolutionary algorithm may be employed to determine definiteness. We provide a proof-of-concept with 16 different distance measures for permutations. Our approach allows to disprove definiteness if a respective counterexample is found. It can also provide an estimate of how likely it is to obtain indefinite kernel matrices. This provides a simple, efficient tool to decide whether additional effort should be spent on designing/selecting a more suitable kernel or algorithm. Copyright © 2018, The Authors. All rights reserved.

关键词： evolutionary algorithms

Fast Exact Computation of Expected HyperVolume Improvement

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arXiv 2018年

作者： Zhao, Guang Arróyave, Raymundo Qian, Xiaoning Department of Electrical and Computer Engineering Texas A&M University Department of Materials Science and Engineering Texas A&M University

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. This paper focuses on the exact calculation of EHVI given a nondominated set, for which the existing exact algorithms are complex and can be inefficient for problems with more than three objectives. Integrating with different decomposition algorithms, we propose a new method for calculating the integral in each decomposed high-dimensional box in constant time. We develop three new exact EHVI calculation algorithms based on three region decomposition methods. The first grid-based algorithm has a complexity of O(m · nm) with n denoting the size of the nondominated set and m the number of objectives. The Walking Fish Group (WFG)based algorithm has a worst-case complexity of O(m · 2n) but has a better average performance. These two can be applied for problems with any m. The third CLM-based (Beume et al., 2009) algorithm is only for m = 3 and asymptotically optimal with complexity Θ(n log n). Performance comparison results show that all our three algorithms are at least twice faster than the state-of-the-art algorithms with the same decomposition methods. When m > 3, our WFG-based algorithm can be over 102 faster than the corresponding existing algorithms. Our algorithm is demonstrated in an example involving efficient multi-objective material design with Bayesian optimization. Copyright © 2018, The Authors. All rights reserved.

关键词： evolutionary algorithms

Self organizing classifiers and niched fitness

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arXiv 2018年

作者： Vargas, Danilo V. Takano, Hirotaka Murata, Junichi Graduate School of Information Science and Electrical Engineering Kyushu University Fukuoka Japan Faculty of Information Science and Electrical Engineering Kyushu University Fukuoka Japan

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 fitness and niching pressures are difficult to balance. Here, a new algorithm called Self Organizing Classifiers is proposed which faces this problem from a different perspective. Instead of balancing the pressures, both pressures are separated and no balance is necessary. In fact, the proposed algorithm possesses a dynamical population structure that self-organizes itself to better project the input space into a map. The niched fitness concept is defined along with its dynamical population structure, both are indispensable for the understanding of the proposed method. Promising results are shown on two continuous multi-step problems. One of which is yet more challenging than previous problems of this class in the literature. Copyright © 2018, The Authors. All rights reserved.

关键词： evolutionary algorithms