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An artist's experience in using an evolutionary algorithm to produce an animated artwork

INTERNATIONAL JOURNAL OF ARTS AND TECHNOLOGY 2011年第2期4卷 155-167页

作者： Trist, Karen Ciesielski, Vic Barile, Perry RMIT Univ GPO Box 2476V Melbourne Vic 3001 Australia

We describe an artist's journey of working with an evolutionary algorithm to create an artwork suitable for exhibition in a gallery. Software based on the evolutionary algorithm produces animations which engage the viewer with a target image slowly emerging from a random collection of greyscale lines. The artwork consists of a grid of movies of eucalyptus tree targets. Each movie resolves with different aesthetic qualities, tempo and energy. The artist exercises creative control by choice of target and values for evolutionary and drawing parameters.

关键词： evolutionary programming evolved art new media art software art animation algorithmic art AI genetic programming genetic art NPR non-photorealistic rendering

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Machine Learning Optimization of Evolvable Artificial Cells

Machine Learning Optimization of Evolvable Artificial Cells

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2nd European Future Technologies Conference and Exhibition (FET)

作者： Caschera, Filippo Rasmussen, Steen Hanczyc, Martin Univ So Denmark DK-5230 Odense Denmark

An evolvable artificial cell is a chemical or biological complex system assembled in laboratory. The system is rationally designed to show life-like properties. In order to achieve an optimal design for the emergence of minimal life, a high dimensional space of possible experimental combinations can be explored. A machine learning approach (Evo-DoE) could be applied to explore this experimental space and define optimal interactions according to a specific fitness function. Herein an implementation of an evolutionary design of experiments to optimize chemical and biochemical systems based on a machine learning process is presented. The optimization proceeds over generations of experiments in iterative loop until optimal compositions are discovered. The fitness function is experimentally measured every time the loop is closed. Two examples of complex systems, namely a liposomal drug formulation and an in vitro cell-free expression system are presented as examples of optimization of molecular interactions in high dimensional space of compositions [1,4]. These represent, for instance, the modules or subsystems that could be optimized by "mixing the protocols" to achieve the high level of sophistication that artificial cells requires. In addition a replication cycle of oil in water emulsions is presented. They represent the container for the artificial cells. (C) Selection and peer-review under responsibility of FET11 conference organizers and published by Elsevier B.V.

关键词： Machine learning experimental design drug design cell-free expression system artificial cells evolutionary programming

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Chaotic Time Series Prediction with Feature Selection Evolution

Chaotic Time Series Prediction with Feature Selection Evolut...

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IEEE Electronics, Robotics and Automotive Mechanics Conference (CERMA)

作者： Landassuri-Moreno, V. Raymundo Marcial-Romero, J. Montes-Venegas, A. Ramos, Marco A. Univ Birmingham Sch Comp Sci Birmingham B15 2TT W Midlands England Univ Autonoma Estado Mexico Fac Ingn Toluca Mexico

ISBN: (纸本)9780769545639

Chaotic time series have been successfully predicted with the EPNet algorithm through the evolution of artificial neural networks. However, the input feature selection problem has either not been fully explored before or has not been compared against other algorithms in the literature. This paper presents four algorithms derived from the classical EPNet algorithm to evolve the input feature selection in three different chaotic series: Logistic, Lorenz and Mackey-Glass. Additionally, some flaws in the prediction field that may be considered in future works are discussed. A comparison against previous work demonstrates that in most cases the specialization of the EPNet algorithm allows better solutions with a smaller number of generations.

关键词： EANNs forecasting evolutionary programming feature selection

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Bilevel Multi-objective Optimization Problem Solving Using Progressively Interactive EMO

Bilevel Multi-objective Optimization Problem Solving Using P...

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6th International Conference on evolutionary Multi-Criterion Optimization

作者： Sinha, Ankur Aalto Univ Sch Econ Dept Business Technol FIN-00076 Helsinki Finland

ISBN: (纸本)9783642198922

Bilevel multi-objective optimization problems are known to be highly complex optimization tasks which require every feasible upper-level solution to satisfy optimality of a lower-level optimization problem. Multi-objective bilevel problems are commonly found in practice and high computation cost needed to solve such problems motivates to use multi-criterion decision making ideas to efficiently handle such problems. Multi-objective bilevel problems have been previously handled using an evolutionary multi-objective optimization (EMO) algorithm where the entire Pareto set is produced. In order to save the computational expense, a progressively interactive EMO for bilevel problems has been presented where preference information from the decision maker at the upper level of the bilevel problem is used to guide the algorithm towards the most preferred solution (a single solution point). The procedure has been evaluated on a set of five DS test problems suggested by Deb and Sinha. A comparison for the number of function evaluations has been done with a recently suggested Hybrid Bilevel evolutionary Multi-objective Optimization algorithm which produces the entire upper level Pareto-front for a bilevel problem.

关键词： Genetic algorithms evolutionary algorithms bilevel optimization multi-objective optimization evolutionary programming multi-criteria decision making hybrid evolutionary algorithms sequential quadratic programming

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Research on Hierarchical Genetic Algorithm Optimized Based on Fuzzy Neural Network 1

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International Conference on Applied Informatics and Communication (ICAIC 2011)

作者： Hao, Yuan Ren, Zhaohui Wang, Bingcheng Northeastern Univ Sch Mech Engn & Automat Shenyang 110004 Liaoning Provin Peoples R China Shenzhen Univ Sch Mech Engn Shenzhen 518040 Peoples R China

ISBN: (数字)9783642232206

ISBN: (纸本)9783642232190

A new algorithm of fuzzy neural network learning is presented. It is based on combining genetic algorithm of hierarchical structure with evolution programming. This algorithm is used to optimize the structure and parameters of fuzzy neural network, reject redundant nodes and redundancy connections, and improve the treatment ability of the network. The results of analysis and experiment show that, by using this method the fuzzy neural network of mechanical fault diagnosis has good concise structure and diagnosis effect.

关键词： Fuzzy neural network Hierarchical genetic algorithm evolutionary programming

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Research on a Model of Order Allocation for Virtual Enterprises

Research on a Model of Order Allocation for Virtual Enterpri...

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Global Conference on Digital Design and Manufacturing Technology

作者： Wang, H. F. Zhejiang Business Technol Inst Sch Continuing Educ Ningbo 315012 Peoples R China

ISBN: (纸本)9783037850718

Industrial clusters can be found very often in the world, particularly in many developing countries. To build virtual enterprise based on an industrial cluster is one of the most important ways to improve the agility and competitiveness of manufacturing enterprises in the cluster. One of the key factors towards the success of virtual enterprises is the correct selection of cooperative partners in the virtual enterprise. An approach of order allocation and partner selection in the environment of industrial clusters is proposed. This approach is composed of two stages: task-resource matching and quantitative evaluation. In the first stage the potential candidates are identified and in the second stage evolutionary programming is applied to deal with partner selection and order allocation problem. The target function, in which the load rate of candidate enterprise is taken as the main variable, is developed, and a simplified example is used to verify the feasibility of the proposed approach. The result suggests that the proposed model and the algorithm obtain satisfactory solutions.

关键词： Order Allocatio Virtual Enterprise Industrial Cluster evolutionary programming

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Evolving Four-Part Harmony Using Genetic Algorithms 1

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Conference on EvoApplications 2011: EvoCOMPLEX, EvoGAMES, EvoIASP, EvoINTELLIGENCE, EvoNUM, AND EvoSTOC

作者： Donnelly, Patrick Sheppard, John Montana State Univ Dept Comp Sci Bozeman MT 59715 USA

ISBN: (数字)9783642205200

ISBN: (纸本)9783642205194

This paper presents a genetic algorithm that evolves a four-part musical composition melodically, harmonically, and rhythmically. Unlike similar attempts in the literature, our composition evolves from a single musical chord without human intervention or initial musical material. The mutation rules and fitness evaluation are based on common rules from music theory. The genetic operators and individual mutation rules are selected from probability distributions that evolve alongside the musical material.

关键词： Genetic Algorithm evolutionary programming Melody Harmony Rhythm Music Composition

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A Study of evolutionary Multiobjective Algorithms and Their Application to Knapsack and Nurse Scheduling Problems

A Study of Evolutionary Multiobjective Algorithms and Their ...

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作者： Le, Khoi Nguyen University of Nottingham

学位级别：博士

evolutionary algorithms (EAs) based on the concept of Pareto dominance seem the most suitable technique for multiobjective optimisation. In multiobjective optimisation, several criteria (usually conflicting) need to be taken into consideration simultaneously to assess a quality of a solution. Instead of finding a single solution, a set of trade-off or compromise solutions that represents a good approximation to the Pareto optimal set is often required. This thesis presents an investigation on evolutionary algorithms within the framework of multiobjective optimisation. This addresses a number of key issues in evolutionary multiobjective optimisation. Also, a new evolutionary multiobjective (EMO) algorithm is proposed. Firstly, this new EMO algorithm is applied to solve the multiple 0/1 knapsack problem (a wellknown benchmark multiobjective combinatorial optimisation problem) producing competitive results when compared to other state-of-the-art MOEAs. Secondly, this thesis also investigates the application of general EMO algorithms to solve real-world nurse scheduling problems. One of the challenges in solving real-world nurse scheduling problems is that these problems are highly constrained and specific-problem heuristics are normally required to handle these constraints. These heuristics have considerable influence on the search which could override the effect that general EMO algorithms could have in the solution process when applied to this type of problems. This thesis outlines a proposal for a general approach to model the nurse scheduling problems without the requirement of problem-specific heuristics so that general EMO algorithms could be applied. This would also help to assess the problems and the performance of general EMO algorithms more fairly.

关键词： evolutionary programming evolutionary computation computer algorithms knapsack problem nurse scheduling

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Robust Image Registration for Multiple Exposure High Dynamic Range Image Synthesis

Robust Image Registration for Multiple Exposure High Dynamic...

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Conference on Image Processing: Algorithms and Systems IX

作者： Yao, Susu ASTAR Inst Infocomm Res Singapore 138632 Singapore

ISBN: (纸本)9780819484079

Image registration is an important preprocessing technique in high dynamic range (HDR) image synthesis. This paper proposed a robust image registration method for aligning a group of low dynamic range images (LDR) that are captured with different exposure times. Illumination change and photometric distortion between two images would result in inaccurate registration. We propose to transform intensity image data into phase congruency to eliminate the effect of the changes in image brightness and use phase cross correlation in the Fourier transform domain to perform image registration. Considering the presence of non-overlapped regions due to photometric distortion, evolutionary programming is applied to search for the accurate translation parameters so that the accuracy of registration is able to be achieved at a hundredth of a pixel level. The proposed algorithm works well for under and over-exposed image registration. It has been applied to align LDR images for synthesizing high quality HDR images.

关键词： Image registration phase correlation phase congruency evolutionary programming HDR imaging

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An Effective Intrusion Detection Algorithm Based on Improved Semi-supervised Fuzzy Clustering

An Effective Intrusion Detection Algorithm Based on Improved...

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International Conference on Intelligent Computing and Information Science

作者： Li, Xueyong Zhang, Baojian Sun, Jiaxia Yan, Shitao Henan Inst Sci & Technol Sch Informat Engn Xinxiang 453003 Peoples R China

ISBN: (纸本)9783642181337

An algorithm for intrusion detection based on improved evolutionary semi-supervised fuzzy clustering is proposed which is suited for situation that gaining labeled data is more difficulty than unlabeled data in intrusion detection systems. The algorithm requires a small number of labeled data only and a large number of unlabeled data and class labels information provided by labeled data is used to guide the evolution process of each fuzzy partition on unlabeled data, which plays the role of chromosome. This algorithm can deal with fuzzy label, uneasily plunges locally optima and is suited to implement on parallel architecture. Experiments show that the algorithm can improve classification accuracy and has high detection efficiency.

关键词： intrusion detection semi-supervised learning clustering evolutionary programming

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