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2010 IEEE World Congress on computational Intelligence

作者： Au, Chun-Kit Leung, Ho-Fung Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781424481262

This paper proposes a modification to the covariance matrix adaptation evolution strategies (CMA-ES). The goal of our modification is to reduce the number of function evaluations to adapt the covariance matrix to the optimal one when the standard CMA-ES is used to optimize convex-quadratic objective functions which have repeated or clustered eigenvalues in their Hessian matrices. By randomly evaluating the minor eigenspace, the modified CMA-ES is evaluated on a standard suite of benchmark problems and its performance is compared with that of the standard CMA-ES. The experimental results show that our proposed modification can improve the performance of the CMA-ES when dominant eigenspaces and minor eigenspaces exist in the Hessian matrices of the underlying objective functions.

关键词： Hessian matrices Hessian matrices convex quadratic objective functions covariance matrices covariance matrix adaptation evolution strategies eigenvalues eigenvalues and eigenfunctions evolutionary computation minor eigenspace random evaluation

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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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Optimizing Player Behavior In A Real-Time Strategy Game Using evolutionary Algorithms

Optimizing Player Behavior In A Real-Time Strategy Game Usin...

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IEEE Congress on evolutionary computation (CEC)

作者： Fernandez-Ares, A. Mora, A. M. Merelo, J. J. Garcia-Sanchez, P. Fernandes, C. Univ Granada Dept Arquitectura & Tecnol Comp E-18071 Granada Spain

ISBN: (纸本)9781424478354

This paper describes an evolutionary Algorithm for evolving the decision engine of a bot designed to play the Planet Wars game. This game, which has been chosen for the Google Artificial Intelligence Challenge in 2010, requires that the artificial player is able to deal with multiple objectives, while achieving a certain degree of adaptability in order to defeat different opponents in different scenarios. The decision engine of the bot is based on a set of rules that have been defined after an empirical study. Then, an evolutionary Algorithm is used for tuning the set of constants, weights and probabilities that define the rules, and, therefore, the global behavior of the bot. The paper describes the evolutionary Algorithm and the results attained by the decision engine when competing with other bots. The proposed bot defeated a baseline bot in most of the playing environments and obtained a ranking position in top-20% of the Google Artificial Intelligence competition.

关键词： Artificial intelligence Engines evolutionary computation Games Genetic algorithms Google Planets

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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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Cooperative Co-evolution and Adaptive Team Composition for a Multi-rover Resource Allocation Problem 1

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25th European Conference on Genetic Programming (EuroGP) Held as Part of EvoStar Conference

作者： Fontbonne, Nicolas Maudet, Nicolas Bredeche, Nicolas Sorbonne Univ ISIR CNRS F-75005 Paris France Sorbonne Univ LIP6 F-75005 Paris France

ISBN: (数字)9783031020568

ISBN: (纸本)9783031020568;9783031020551

In this paper, we are interested in ad hoc autonomous agent team composition using cooperative co-evolutionary algorithms (CCEA). In order to accurately capture the individual contribution of team agents, we propose to limit the number of agents which are updated in-between team evaluations. However, this raises two important problems with respect to (1) the cost of accurately estimating the marginal contribution of agents with respect to the team learning speed and (2) completing tasks where improving team performance requires multiple agents to update their policies in a synchronized manner. We introduce a CCEA algorithm that is capable of learning how to update just the right amount of agents' policies for the task at hand. We use a variation of the El Farol Bar problem, formulated as a multi-robot resource selection problem, to provide an experimental validation of the algorithms proposed.

关键词： ad hoc autonomous agent teams multi-agent systems marginal contribution team composition multi-robots cooperative co-evolutionary algorithms (CCEA) evolutionary computation evolutionary robotics

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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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Diagnosis of small-signal parameters for broadband amplifiers through S-parameter measurements and sensitivity-guided evolutionary search 04

Diagnosis of small-signal parameters for broadband amplifier...

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International Conference on Computer Aided Design (ICCAD 2004)

作者： Liu, F Ozev, S Brooke, M Duke Univ Durham NC 27708 USA

ISBN: (纸本)0780387023

With increasing uncertainties in the modeling and processing of semiconductor devices, it is essential that the sources of failures be identified once the devices are manufactured In this paper, we present a methodology to diagnose the problems in broad-band amplifiers by determining the most important small signal parameters of the internal transistors. We use an evolutionary algorithm specifically designed to mimic the expected errors to ensure fast convergence to the correct solution. Sensitivity analysis is used to determine the set of the most impactful small signal parameters and to guide the evolutionary search. Experimental results indicate the proposed algorithm determines the parameters accurately and it scales well in terms of accuracy and computation time.

关键词： Broadband amplifiers Scattering parameters Semiconductor devices Manufacturing processes Virtual manufacturing Semiconductor device manufacture evolutionary computation Algorithm design and analysis Error correction Sensitivity analysis

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A Primary Study on Hyper-Heuristics Powered by Artificial Neural Networks for Customising Population-based Metaheuristics in Continuous Optimisation Problems

A Primary Study on Hyper-Heuristics Powered by Artificial Ne...

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IEEE Congress on evolutionary computation (CEC)

作者： Tapia-Avitia, Jose M. Cruz-Duarte, Jorge M. Amaya, Ivan Carlos Ortiz-Bayliss, Jose Terashima-Marin, Hugo Pillay, Nelishia Tecnol Monterrey Monterrey Mexico Univ Pretoria Pretoria South Africa

ISBN: (数字)9781665467087

ISBN: (纸本)9781665467087

Metaheuristics (MHs) are proven powerful algorithms for solving non-linear optimisation problems over discrete, continuous, or mixed domains. Applications have ranged from basic sciences to applied technologies. Nowadays, the literature contains plenty of MHs based on exceptional ideas, but often, they are just recombining elements from other techniques. An alternative approach is to follow a standard model that customises population-based MHs, utilising simple heuristics extracted from well-known MHs. Different approaches have explored the combination of such simple heuristics, generating excellent results compared to the generic MHs. Nevertheless, they present limitations due to the nature of the metaheuristic used to study the heuristic space. This work investigates a field of action for implementing a model that takes advantage of previously modified MHs by learning how to boost the performance of the tailoring process. Following this reasoning, we propose a hyper-heuristic model based on Artificial Neural Networks (ANNs) trained with processed sequences of heuristics to identify patterns that one can use to generate better MHs. We prove the feasibility of this model by comparing the results against generic MHs and other approaches that tailor unfolded MHs. Our results evidenced that the proposed model outperformed an average of 84% of all scenarios;in particular, 89% of basic and 77% of unfolded approaches. Plus, we highlight the configurable capability of the proposed model, as it shows to be exceptionally versatile in regards to the computational budget, generating good results even with limited resources.

关键词： Hyper-heuristic Artificial Neural Networks Search Operators Optimisation evolutionary computation

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Decomposing Large-Scale Capacitated Arc Routing Problems using a Random Route Grouping Method

Decomposing Large-Scale Capacitated Arc Routing Problems usi...

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IEEE Congress on evolutionary computation

作者： Mei, Yi Li, Xiaodong Yao, Xin RMIT Univ Sch Comp Sci & Informat Technol Melbourne Vic 3000 Australia Univ Birmingham Sch Comp Sci Birmingham B15 2TT W Midlands England

ISBN: (纸本)9781479904549;9781479904532

In this paper, a simple but effective Random Route Grouping (RRG) scheme is developed to decompose the Large-Scale Capacitated Arc Routing Problem (LSCARP). A theoretical analysis is given to show that the decomposition is guaranteed to be improved by RRG along with the improvement of the best-sofar solution during the search process. Then, RRG is combined with a cooperative co-evolution model to solve LSCARP. The experimental results on the EGL-G LSCARP set showed that given the same computational budget, the proposed approach obtained much better results than its counterpart without using decomposition.

关键词： evolutionary computation random processes search problems set theory vehicle routing

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