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Implementing Phenotypic Plasticity with an Adaptive Generative Representation 16

Implementing Phenotypic Plasticity with an Adaptive Generati...

16th IEEE International Conference on computational Intelligence in Bioinformatics and computational Biology (IEEE CIBCB)

作者： Ashlock, Daniel Ashlock, Wendy Montgomery, James Univ Guelph Dept Math & Stat Guelph ON Canada Ashlock & McGuinness Consulting Guelph ON Canada Univ Tasmania Sch Technol Environm & Design Hobart Tas Australia

ISBN: (纸本)9781728114620

This study compares an adaptive and a non-adaptive representation for finding long walks on obstructed grids. This process models adaption of a simple plant to an environment where the plant's ability to grow is impeded by obstructions such as resource poor areas like bare rock. The intent of the adaptive representation is to model the biological phenomenon of phenotypic plasticity in which gene regulation is at least partially in response to environmental cues, in this case the obstructions. The adaptive representation is found to have a substantial advantage, with the greatest level of advantage at intermediate levels of obstruction. Agents are asked to solve multiple problem instances simultaneously (i.e. using the same chromosome). The advantage of the adaptive representation is also found to be higher when more boards are used in fitness evaluation.

关键词： Standards Surface acoustic waves Encoding Biological cells Adaptation models Evolution (biology) evolutionary computation

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A Hybrid Ensemble Learning Model for Short-Term Solar Irradiance Forecasting Using Historical Observations and Sky Images

A Hybrid Ensemble Learning Model for Short-Term Solar Irradi...

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IEEE IAS Conference on Industrial and Commercial Power System Asia (IEEE I and CPS Asia)

作者： Wang, Zhongju Wang, Long Huang, Chao Luo, Xiong Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing 100083 Peoples R China Univ Sci & Technol Beijing Shunde Grad Sch Foshan 528300 Peoples R China Beijing Key Lab Knowledge Engn Mat Sci Beijing 100083 Peoples R China

ISBN: (纸本)9781665434980

A hybrid multi-modal ensemble learning model is proposed for short-term solar irradiance forecasting based on historical observations and sky images in this paper. In the proposed model, the historical time series of solar irradiance is utilized to extract temporal characteristics. Meanwhile, the sky camera images are used as an exogenous input to offer the cloud cover information. A powerful ensemble learning model, Extreme Gradient Boosting (XGBoost), is employed to capture the function relationships between input features and future observations. Since the mean squared error loss is sensitive to the extreme large or small historical irradiance, a novel loss function is proposed to improve the robustness of XGBoost. In order to find out the best controlling parameters, Rao-1 algorithm is employed due to its fast search capability. To validate the performance of the proposed method, a solar irradiance dataset containing three-year historical observations and ground-based sky camera images collected from the Folsom is employed. Meanwhile, four commonly applied methods, Lasso, Ridge regression, support vector regression and boosted regression trees, are considered as benchmarking methods. The forecasting horizons from 5 to 30 min are considered for all compared methods while two metrics, mean absolute error and root mean squared error, are computed. Experimental results prove that the proposed hybrid model has better forecasting performance compared with benchmarking methods over all forecasting horizons.

关键词： XGBoost Forecasting Solar Irradiance evolutionary computation

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evolutionary Algorithmic Deployment of Radio Beacons for Indoor Positioning

Evolutionary Algorithmic Deployment of Radio Beacons for Ind...

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IEEE Congress on evolutionary computation (CEC) held as part of IEEE World Congress on computational Intelligence (IEEE WCCI)

作者： Ye, Sipan Chen, Yuanzhu Hu, Ting Mem Univ Newfoundland Dept Comp Sci St John NF A1B 3X5 Canada

ISBN: (纸本)9781509006229

In mobile computing, the location awareness of a mobile device and its user enables numerous personalized and social services such as recommendation of products and sharing current locations on social networks. Extending positioning services to indoor environments augments the value of the mobile communication market vastly. Due to serious signal attenuation, navigation satellites are incapable, and a common approach is to use or deploy small-scale radio frequency transmitters. When deploying these radio beacons, it is crucial to use a small number of them to provide high-quality positioning services. Such a deployment task is a challenging optimization problem, and the system administrators would benefit from having a spectrum of solutions with varying balance between cost and quality. In this study, we propose an evolutionary Algorithm (EA) to tackle the problem. Using a cost-quality adjustment parameter, our EA framework is able to provide a set of solution options to meet varying requirements balancing cost and quality. This property is a result of the parallel population-based search of EAs, and can be very useful in real-world engineering applications.

关键词： Mobile computing evolutionary computation Location-aware service Indoor positioning Radio beacons Genetic algorithm Multiple-objective optimization

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A Dynamic Multiobjective evolutionary Algorithm for Multicast Routing Problem

A Dynamic Multiobjective Evolutionary Algorithm for Multicas...

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25th IEEE International Conference on Tools with Artificial Intelligence (ICTAI)

作者： Bueno, Marcos L. P. Oliveira, Gina M. B. Univ Fed Uberlandia Sch Comp Sci Uberlandia MG Brazil

ISBN: (纸本)9781479929719

In this work, we propose an evolutionary algorithm to tackle a multiobjective optimization problem, namely a constrained multicast routing with quality demands. The proposed algorithm embeds two different strategies along with SPEA2 (Strength Pareto evolutionary Algorithm 2) method attempting to improve convergence to Pareto front. These strategies are a heuristic for population diversity augmentation and a neighborhood mating selection scheme. Experimental results showed that selecting which strategy to use depends on population dynamics aspects described by non dominated solutions over evolutionary iterations. It was possible to observe that the proposed mechanism can help the algorithm to achieve better solutions over convergence and diversity goals in most cases.

关键词： evolutionary computation multiobjective optimization multicast routing pareto optimality

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A No-Free-Lunch theorem for non-uniform distributions of target functions

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Journal of Mathematical Modelling and Algorithms 2004年第4期3卷 313-322页

作者： Igel, Christian Toussaint, Marc Department of Theoretical Biology Institut für Neuroinformatik Ruhr-Universität Bochum 44780 Bochum Germany

The sharpened No-Free-Lunch-theorem (NFL-theorem) states that, regardless of the performance measure, the performance of all optimization algorithms averaged uniformly over any finite set F of functions is equal if and only if F is closed under permutation (c.u.p.). In this paper, we first summarize some consequences of this theorem, which have been proven recently: The number of subsets c.u.p. can be neglected compared to the total number of possible subsets. In particular, problem classes relevant in practice are not likely to be c.u.p. The average number of evaluations needed to find a desirable (e.g., optimal) solution can be calculated independent of the optimization algorithm in certain scenarios. Second, as the main result, the NFL-theorem is extended. Necessary and sufficient conditions for NFL-results to hold are given for arbitrary distributions of target functions. This yields the most general NFL-theorem for optimization presented so far. © 2004 Kluwer Academic Publishers.

关键词： evolutionary computation No-Free-Lunch theorem

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A Quantum-Inspired Fuzzy Based evolutionary Algorithm for Data Clustering

A Quantum-Inspired Fuzzy Based Evolutionary Algorithm for Da...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Patel, Om Prakash Bharill, Neha Tiwari, Aruna Indian Inst Technol Dept Comp Sci & Engn Indore 453331 Madhya Pradesh India

ISBN: (纸本)9781467374286

In this paper, a Quantum-Inspired evolutionary Fuzzy C-Means (QIE-FCM) algorithm is proposed. The proposed approach find the true number of clusters and the appropriate value of weighted exponent (m) which is required to be known in advance to perform clustering using Fuzzy C-Means (FCM) algorithm. However, the selection of inappropriate value of m and C may lead the algorithm to converge to the local optima. To address the issue of selecting the appropriate value of m and corresponding value of C. In QIE-FCM, the quantum concept is used in classical computer where m is represented in terms of quantum bits (qubits). The QIE-FCM is based on generations. At each generation (g), quantum gates are used to generate a new value of m. For each generated value of m, FCM algorithm is executed by varying values of C. Then, corresponding to m value appropriate value of C is identified by evaluating local fitness function for generation g. To achieve the global best value of m and C, the global fitness function is evaluated by comparing the local best fitness value in current generation with the best fitness value obtained among all the previous generations. To judge the efficacy of QIE-FCM algorithm, it is compared with two well-known indices and three evolutionary fuzzy based clustering algorithm and their performance is evaluated on four benchmark datasets. Furthermore, the sensitivity of QIE-FCM is also experimentally investigated in this paper.

关键词： evolutionary computation fuzzy set theory pattern clustering quantum gates QIE-FCM algorithm benchmark datasets best fitness value data clustering global best value global fitness function local best fitness value local fitness function local optima performance evaluation quantum bits quantum-inspired evolutionary fuzzy c-means algorithm quantum-inspired fuzzy based evolutionary algorithm qubits weighted exponent value Algorithm design and analysis Clustering algorithms Computer science evolutionary computation Optimization Partitioning algorithms Quantum computing

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Dynamic behaviour forecast as a driving force in the coevolution of one-dimensional cellular automata 7

Dynamic behaviour forecast as a driving force in the coevolu...

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7th Brazilian Symposium on Neural Networks (SBRN 02)

作者： Oliveira, GMB Asakura, OKN de Oliveira, PPB Univ Presbiteriana Mackenzie Posgrad Engn Eletr Sao Paulo Brazil

ISBN: (纸本)0769517099

Various evolutionary methods have been used to look for cellular automata (CA) with a predefined computational behaviour. The most widely studied CA task is the Density Classification Task (DCT) and the best rule currently known for it was obtained by a coevolutionary genetic algorithm (CGA). Here, we analyse the influence of incorporating a parameter-based heuristic into the coevolutionary search. The results obtained show that the parameters can effectively help a CGA in searching for DCT rules, and suggest that the choice of the amount of bias in the search, allowed for the heuristic, is more sensitive than in previous uses we made of it within standard evolutionary algorithms.

关键词： Algorithm design and analysis Biology computing Concurrent computing Discrete cosine transforms Evolution (biology) evolutionary computation Genetic algorithms High performance computing Parallel processing Search methods

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Geometric Semantic Genetic Programming Using External Division of Parents 4

Geometric Semantic Genetic Programming Using External Divisi...

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IIAI 4th International Congress on Advanced Applied Informatics (IIAI-AAI)

作者： Hara, Akira Kushida, Jun-ichi Kisaka, Kei Takahama, Tetsuyuki Hiroshima City Univ Grad Sch Informat Sci Asaminami Ku 3-4-1 Ozuka Higashi Hiroshima 7313194 Japan Hiroshima City Univ Fac Informat Sci Hiroshima Japan

ISBN: (纸本)9781479999583

In this paper, we focus on symbolic regression problems, in which we find functions approximating the relationships between given input and output data. If we do not have the knowledge on the structure (e.g. degree) of the true functions, Genetic Programming (GP) is often used for evolving tree structural numerical expressions. In GP, crossover operator has a great influence on the quality of the acquired solutions. Therefore, various crossover operators have been proposed. Recently, new crossover operators based on semantics of tree structures have attracted many attentions for efficient search. In the semantics-based crossover, offspring is created from its parental individuals so that the offspring can be similar to the parents not structurally but semantically. Geometric Semantic Genetic Programming (GSGP) is a method in which offspring is produced by a convex combination of two parental individuals. This operation corresponds to the internal division of two parents. This method can optimize solutions efficiently because the crossover operator always produces better solution than a worse parent. But, in GSGP, if the true function exists outside of two parents in semantic space, it is difficult to produce better solution than both of the parents. In this paper, we propose an improved GSGP which can also consider external divisions as well as internal ones. By comparing the search performance among several crossover operators in symbolic regression problems, we showed that our methods are superior to the standard GP and conventional GSGP.

关键词： evolutionary computation Genetic Programming Symbolic Regression

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Saliency Improvement Through Genetic Programming 18

Saliency Improvement Through Genetic Programming

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3rd International Workshop on Interactive and Spatial Computing (IWISC)

作者： Martinez-Rodriguez, Diana E. Contreras-Cruz, Marco A. Hernandez-Belmonte, Uriel H. Bereg, Sergey Ayala-Ramirez, Victor Univ Guanajuato DICIS Guanajuato Mexico Univ Texas Dallas Richardson TX 75083 USA

ISBN: (纸本)9781450354394

Visual saliency detection aims at finding regions of interest which contain relevant information in images. In the last years, several saliency methods have been proposed, however, it is still a challenging task in visualization, graphics and computer vision. Visual saliency has been useful in many tasks such as object segmentation, object detection, image retrieval, place recognition, human-computer interaction, among others. In this work, we present the design of a Genetic Programming Framework to improve the saliency maps generated from a determined saliency method. As output, we obtain a sequence of operators to improve a saliency map. We have tested this approach by using three saliency methods of the state-of-the-art. The validation of the generated solutions have been tested in three visual saliency image datasets. The results of the experiments show that the solution found by Genetic Programming outperforms the original input saliency model.

关键词： Saliency Enhancement Visual Saliency evolutionary computation Salient Object Detection

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The Chaos-based Shuffled Frog Leaping Algorithm and Its Application

The Chaos-based Shuffled Frog Leaping Algorithm and Its Appl...

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4th International Conference on Natural computation (ICNC 2008)

作者： Li, Yinghai Zhou, Jianzhong Yang, Junjie Liu, Li Qin, Hui Yang, Li Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Hubei Peoples R China

ISBN: (纸本)9780769533049

Shuffled Frog Leaping Algorithm (SFLA) is a ne, meta-heuristic evolutionary algorithm with simple algorithm structure and fast calculation speed In this paper, a novel algorithm based on SFLA and chaos search is presented. This algorithm uses chaos search to generate neighborhoods of extremum so as to maintain solution diversity and get rid of local optimal solution when the individual stops evolution. The numerical experiments results show it outperforms standard SFLA. Finally, the proposed algorithm is used to solve the problem of mid-long term optimal operation of cascade hydropower stations and is compared with other two algorithms. The operation results show its feasibility and high efficiency.

关键词： Chaos Hydroelectric power generation Diversity reception Testing Partitioning algorithms evolutionary computation Bridges Assembly Food technology Water resources

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