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2017 Genetic and evolutionary Computation Conference Companion, GECCO 2017

作者： Mueller-Bady, Robin Kappes, Martin Atkinson, Lukas Medina-Bulo, Inmaculada Frankfurt University of Applied Sciences Nibelungenplatz 1 Frankfurt60318 Germany Universidad de Ćadiz Avda. de la Universidad de Ćadiz 10 Puerto Real11519 Spain

ISBN: (纸本)9781450349390

An important challenge in designing evolutionary search heuristics is the statistically significant evaluation of different configurations. The goal is to find an optimal algorithm design with respect to its parameters, i.e., parameter tuning. In this paper, we propose an open source software framework, called Multijob, allowing to simplify and automate EA configuration and parameter tuning. Additionally, the framework offers a workflow for distributed execution of the preconfigured algorithms in heterogeneous computing clusters or grids. The framework uses features of the Unix-based command line utility GNU Parallel, which enables the pausing and resuming of jobs, estimation of experiment completion time, etc. It is highly dynamic due to its language-Agnosticism and flexible with respect to parameters and configurations of specific EAs. The possibility of distributing computing time among (heterogeneous) hardware, only requiring access over secure shell (SSH) and a proper environment for job execution, makes Multijob a noteworthy utility for improving efficiency of statistically significant parameter testing and tuning. © 2017 ACM.

关键词： evolutionary algorithms

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An application of stochastic differential equations to evolutionary algorithms 17

An application of stochastic differential equations to evolu...

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14th ACM/SIGEVO Workshop on Foundations of Genetic algorithms, FOGA 2017

作者： Paixão, Tiago Heredia, Jorge Pérez Institute of Science and Technology Austria Am Campus 1 Klosterneuburg3400 Austria University of Sheffield SheffieldS1 4DP United Kingdom

ISBN: (纸本)9781450346511

There has been renewed interest in modelling the behaviour of evolutionary algorithms (EAs) by more traditional mathematical objects, such as ordinary differential equations or Markov chains. The advantage is that the analysis becomes greatly facilitated due to the existence of well established methods. However, this typically comes at the cost of disregarding information about the process. Here, we introduce the use of stochastic differential equations (SDEs) for the study of EAs. SDEs can produce simple analytical results for the dynamics of stochastic processes, unlike Markov chains which can produce rigorous but unwieldy expressions about the dynamics. On the other hand, unlike ordinary differential equations (ODEs), they do not discard information about the stochasticity of the process. We show that these are especially suitable for the analysis of fixed budget scenarios and present analogs of the additive and multiplicative drift theorems for SDEs. We exemplify the use of these methods for two model algorithms ((1+1) EA and RLS) on two canonical problems (OneMax and LeadingOnes). © 2017 Copyright held by the owner/author(s).

关键词： evolutionary algorithms

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On the use of the dual formulation for minimum weighted vertex cover in evolutionary algorithms 17

On the use of the dual formulation for minimum weighted vert...

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14th ACM/SIGEVO Workshop on Foundations of Genetic algorithms, FOGA 2017

作者： Pourhassan, Mojgan Friedrich, Tobias Neumann, Frank Optimisation and Logistics School of Computer Science University of Adelaide Adelaide Australia Algorithm Engineering Hasso Plattner Institute Potsdam Germany

ISBN: (纸本)9781450346511

We consider the weighted minimum vertex cover problem and investigate how its dual formulation can be exploited to design evolutionary algorithms that provably obtain a 2-approximation. Investigating multi-valued representations, we show that variants of randomized local search and the (1+1) EA achieve this goal in expected pseudo-polynomial time. In order to speed up the process, we consider the use of step size adaptation in both algorithms and show that RLS obtains a 2-approximation in expected polynomial time while the (1+1) EA still encounters a pseudo-polynomial lower bound. © 2017 Copyright held by the owner/author(s).

关键词： evolutionary algorithms

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Learning to describe collective search behavior of evolutionary algorithms in solution space 11th

Learning to describe collective search behavior of evolution...

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11th International Conference on Simulated Evolution and Learning, SEAL 2017

作者： Liu, Lei Pang, Chengshan Liu, Weiming Li, Bin School of Information Science and Technology University of Science and Technology of China HefeiAnhui China

ISBN: (纸本)9783319687582

evolutionary algorithms (EAs) are a kind of population-based meta-heuristic optimization methods, which have proven to have superiorities in solving NP-complete and NP-hard optimization problems. But until now, there is lacking in the researches of effective representation method to describe the collective search behavior of the evolutionary Algorithm, while it is useful for researchers and engineers to understand and compare different EAs better. In the past, most of the theoretical researches cannot directly guide for practical applications. To bridge the gap between theoretical research and practice, we present a generic and reusable framework for learning features to describe collective behavior of EAs in this paper. Firstly, we represent the collective behavior of EAs with a parent-child difference of population distribution encoded by self-organizing map (SOM). Then, we train a Convolutional Neural Network (CNN) to learn problem-invariant features from the samples of EAs’ collective behavior. Lastly, experiment results demonstrate that our framework can effectively learn discriminative features representing collective behavior of EAs. In the behavioral feature space stretched by the obtained features, the collective behavior samples of various EAs on various testing problems exhibit obvious aggregations that highly correlated with EAs but very weakly related to testing problems. We believe that the learned features are meaningful in analyzing EAs, i.e. it can be used to measure the similarity of EAs according to their inner behavior in solution space, and further guide in selecting an appropriate combination of sub-algorithm of a hybrid algorithm according to the diversity of candidate sub-algorithm instead of blind. © Springer International Publishing AG 2017.

关键词： evolutionary algorithms

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Termination criteria in evolutionary algorithms: A survey 9

Termination criteria in evolutionary algorithms: A survey

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9th International Joint Conference on Computational Intelligence, IJCCI 2017

作者： Ghoreishi, Seyyedeh Newsha Clausen, Anders Joergensen, Bo Noerregaard Center for Energy Informatics Maersk Mc-Kinney Moller Institute University of Southern Denmark Odense M Denmark

ISBN: (纸本)9789897582745

Over the last decades, evolutionary algorithms have been extensively used to solve multi-objective optimization problems. However, the number of required function evaluations is not determined by nature of these algorithms which is often seen as a drawback. Therefore, a robust and reliable termination criterion is needed to stop the algorithm. There is a huge amount of knowledge encapsulated in the studies targeting termination criteria in evolutionary algorithms, but an updated integrated overview of this knowledge is missing. For this reason, we aim to conduct a systematic research through a comprehensive literature study. We extended the basic categorization of termination criteria to a more advanced one that takes the most common used termination criteria into consideration based on their specifications and the way they have been evolved over time. The survey is concluded by suggesting a road-map for future research directions. Copyright © 2017 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.

关键词： evolutionary algorithms

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Using evolutionary algorithms to design energy supply systems for a changing building stock 15

Using evolutionary algorithms to design energy supply system...

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15th International Conference of the International Building Performance Simulation Association, Building Simulation 2017

作者： Mans, Michael Remmen, Peter Scholl, Anna Fuchs, Marcus Müller, Dirk RWTH Aachen University E.ON Energy Research Center Institute for Energy Efficient Buildings and Indoor Climate Germany

ISBN: (纸本)9781510870673

Central energy supply units for district heating grids need to be planned for a relatively long usage due to large investments. During this long usage period, building retrofits may change the demand patterns of the building stock. These changes might have an effect on the used energy supply units. As static tools do not adequately address this, we propose to use dynamic building simulation for individual buildings to determine the future heat demand. In order to find good solutions for a retrofit order of the buildings and design of energy supply systems that meet the given technical requirements two individual optimizations are performed, both using evolutionary algorithms (EA). The methodology is applied to a research campus with over 200 buildings. © 2017 Building Simulation Conference Proceedings. All rights reserved.

关键词： evolutionary algorithms

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A Survey of Multiobjective evolutionary algorithms 20

A Survey of Multiobjective Evolutionary Algorithms

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20th IEEE International Conference on Computational Science and Engineering and 15th IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, CSE and EUC 2017

作者： Zhang, Jiawei Xing, Lining School of Management Science and Engineering National University of Defense Technology Changsha China

ISBN: (纸本)9781538632215

Multiobjective optimization aims to simultaneously optimize two or more objectives for a problem, with multiobjective evolutionary algorithms (MOEAs) having become a popular research topic in evolutionary multiobjective optimization. We first define the multiobjective optimization problem and briefly summarize multiobjective optimization methods based on the evolutionary algorithm. Representative MOEAs from three categories are then introduced in detail, and we discuss some of the problems and challenges in improving MOEAs. Finally, future research directions for MOEAs are proposed. © 2017 IEEE.

关键词： evolutionary algorithms

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Runtime analysis of population-based evolutionary algorithms1: Introductory tutorial at GECCO 2017

Runtime analysis of population-based evolutionary algorithms...

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2017 Genetic and evolutionary Computation Conference Companion, GECCO 2017

作者： Lehre, Per Kristian Oliveto, Pietro S. University of Birmingham BirminghamB15 2TT United Kingdom University of Sheffield SheffieldS1 4DP United Kingdom

ISBN: (纸本)9781450349390

Runtime analysis of evolutionary algorithms mathematically rigorous statements about EA performance most previous results on simple EAs, such as (1+1) EA special techniques developed for population-based EAs Level-based method Corus et al. [2014] EAs analysed from the perspective of EDAs Upper bounds on expected optimisation time Example applications include crossover and noise Negative drift theorem Lehre [2011a] reproductive rate vs selective pressure exponential lower bounds mutation-selection balance Diversity + Bandwidth analysis for fitness proportional selection analysis of crossover low selection pressure exponential lower bounds Speed-up via crossover for steady state GAs to escape local Optima.

关键词： evolutionary algorithms

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The applications of jumping genes inspired evolutionary algorithms in microwave circuit designs

The applications of jumping genes inspired evolutionary algo...

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2017 International Applied Computational Electromagnetics Society Symposium in China, ACES-China 2017

作者： Zheng, Shao Yong Sun Yat-sen University Higher Education Mega Centre No.132 Waihuan East Road Guangzhou510006 China

ISBN: (纸本)9780996007856

The optimization of a microwave circuit is a complex multi-objective problem (MOP) needed to be effectively solved. Multi-objective evolution algorithms (MOEAs) are efficient in dealing with MOPs because of their population property inspired by the natural evolution of species. Exploitation and exploration are of equal importance to MOEAs for approximating the optimal Pareto front (PF) well. However, most of them are focus on exploitation, and may result in poor diversity or stick at local optimal front. Jumping genes operator (JG) inspired from a biological discovery was introduced in different evolutionary algorithms to maintain the balance between in convergence and diversity. In this paper, the principle, applications and future directions for jumping genes inspired evolutionary algorithm are summarized. © 2017 Applied Computational Electromagnetics Society.

关键词： evolutionary algorithms

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evolutionary algorithms for supply chain management INTRODUCTION

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ANNALS OF OPERATIONS RESEARCH 2016年第2期242卷 195-206页

作者： Govindan, Kannan Univ Southern Denmark Dept Technol & Innovat Ctr Engn Operat Management Odense Denmark

An introduction to the special issue of the journal is presented in which the editor discusses the trends of using evolutionary algorithms (EAs) in supply chain management (SCM), the role of Pareto analysis in EAs, an... 详细信息

关键词： evolutionary algorithms SUPPLY chain management PARETO analysis

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