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检索条件"主题词=evolutionary computation"

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Concurrent Evolution of Hardware and Software for Application-Specific Microprogrammed Systems

Concurrent Evolution of Hardware and Software for Applicatio...

引用

IEEE International Conference on Evolvable Systems (ICES)

作者： Minarik, Milos Sekanina, Lukas Brno Univ Technol Fac Informat Technol CS-61090 Brno Czech Republic

ISBN: (纸本)9781467358699

Embedded systems often have to calculate some mathematical functions using iterative algorithms. When hard constraints are specified in terms of the area on the chip a possible solution is to implement the iterative algorithm by means of a microprogrammed digital circuit. In this paper, the first version of a new design framework is presented to automate the design and optimization of such microprogrammed systems. The framework utilizes evolutionary design and optimization techniques to find the most suitable implementation of the hardware architecture as well as the program for the programmable logic controller. The functionality of the proposed approach is evaluated using evolutionary design of three HW/SW systems under different constraints.

关键词： evolutionary computation genetic algorithms hardware-software codesign linear programming programmable controllers

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Stable Feature Selection for Gene Expression using Enhanced Binary Particle Swarm Optimization 12

Stable Feature Selection for Gene Expression using Enhanced ...

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12th International Conference on Agents and Artificial Intelligence (ICAART)

作者： Dhrif, Hassen Wuchty, Stefan Univ Miami Comp Sci Coral Gables FL 33124 USA

ISBN: (纸本)9789897583957

Feature subset selection (FSS) is an intractable optimization problem in high-dimensional gene expression datasets, leading to an explosion of local minima. While binary variants of particle swarm optimization (BPSO) have been applied to solve the FSS problem, increasing dimensionality of the feature space pose additional challenges to these techniques imparing their ability to select most relevant feature subsets in the massive presence of uninformative features. Most FSS optimization techniques focus on maximizing classification performance while minimizing subset size but usually fail to account for solution stability or feature relevance in their optimization process. In particular, stability in FSS is interpreted differently compared to PSO. Although a large volume of published studies on each stability issue separately exists, wrapper models that tackle both stability problems at the same time are still missing. Specifically, we introduce a novel appraoch COMBPSO (COMBinatorial PSO) that features a novel fitness function, integrating feature relevance and solution stability measures with classification performance and subset size as well as PSO adaptations to enhance the algorithm's convergence abilities. Applying our approach to real disease-specific gene expression data, we found that COMBPSO has similar classification performance compared to BPSO, but provides reliable classification with considerably smaller and more stable gene subsets.

关键词： Feature Selection Stability Scalability Particle Swarm Optimization evolutionary computation Gene Discovery

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Evolving Agents for the Hanabi 2018 CIG Competition 14

Evolving Agents for the Hanabi 2018 CIG Competition

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14th IEEE Conference on computational Intelligence and Games (CIG)

作者： Canaan, Rodrigo Shen, Haotian Tornado, Ruben Togelius, Julian Nealen, Andy Menzel, Stefan NYU New York NY 10012 USA Honda Res Inst Europe GmbH Offenbach Germany

ISBN: (纸本)9781538643594

Hanabi is a cooperative card game with hidden information that has won important awards in the industry and received some recent academic attention. A two-track competition of agents for the game will take place in the 2018 CIG conference. In this paper, we develop a genetic algorithm that builds rule-based agents by determining the best sequence of rules from a fixed rule set to use as strategy. In three separate experiments, we remove human assumptions regarding the ordering of rules, add new, more expressive rules to the rule set and independently evolve agents specialized at specific game sizes. As result, we achieve scores superior to previously published research for the mirror and mixed evaluation of agents.

关键词： artificial intelligence games evolutionary computation

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An Embodied Approach for Evolving Robust Visual Classifiers 15

An Embodied Approach for Evolving Robust Visual Classifiers

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17th Genetic and evolutionary computation Conference (GECCO)

作者： Zieba, Karol Bongard, Josh Univ Vermont Dept Comp Sci Burlington VT 05401 USA

ISBN: (纸本)9781450334723

Despite recent demonstrations that deep learning methods can successfully recognize and categorize objects using high dimensional visual input, other recent work has shown that these methods can fail when presented with novel input. However, a robot that is free to interact with objects should be able to reduce spurious differences between objects belonging to the same class through motion and thus reduce the likelihood of over fitting. Here we demonstrate a robot that achieves more robust categorization when it evolves to use proprioceptive sensors and is then trained to rely increasingly on vision, compared to a similar robot that is trained to categorize only with visual sensors. This work thus suggests that embodied methods may help scaffold the eventual achievement of robust visual classification.

关键词： evolutionary computation fitness deception scaffolding

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LARGE TARGET-WEAPON ASSIGNMENT PROBLEMS WITH evolutionary PROGRAMMING AS APPLIED TO THE ROYAL THAI ARMED FORCES GROUND-TO-GROUND ROCKET SIMULATION SYSTEM

LARGE TARGET-WEAPON ASSIGNMENT PROBLEMS WITH EVOLUTIONARY PR...

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10th International Conference on Modeling and Applied Simulation (MAS 2011)

作者： Malisuwan, Settapong Nandhakwang, Teeranan Veeraklaew, Tawiwat Def Technol Inst Lab Mech & Aeronaut Engn Bangkok Thailand Natl Broadcasting & Telecommun Commiss Dynam Spectrum Management Project Bangkok Thailand

ISBN: (纸本)9788890372452

An algorithm based on evolutionary programming (EP) is developed and presented for large numbers of target-weapon assignment. An optimal assignment scheduling in one, which allocates target to weapon such that the total expected of target surviving the defense, is minimized. The proposed method improves EP with reordered mutation operator to handle a large-scale assignment problem. The main advantage of this approach is that the computation time can be controlled via tradeoff performance between the computation time and target surviving value.

关键词： Assignment problem Large-scale problems evolutionary computation evolutionary programming Reordered mutation Target-weapon assignment problem

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Real-Parameter Optimization with OptBees

Real-Parameter Optimization with OptBees

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IEEE Congress on evolutionary computation (CEC)

作者： Maia, Renato Dourado de Castro, Leandro Nunes Caminhas, Walmir Matos Univ Estadual Montes Claros Dept Comp Sci Montes Claros MG Brazil Univ Prebiteriana Mackenzie Grad Program Elect Engn Nat Comp Lab LCoN Sao Paulo Brazil Univ Fed Minas Gerais Dept Elect Engn Computat Intelligence Lab Belo Horizonte MG Brazil

ISBN: (纸本)9781479914883

This paper reports how OptBees, an algorithm inspired by the collective decision-making of bee colonies, performed in the test bed developed for the Special Session & Competition on Real-Parameter Single Objective Optimization at CEC-2014. The test bed includes 30 scalable functions, many of which are both non-separable and highly multi-modal. Results include OptBees' performance on the 10, 30, 50 and 100-dimensional versions of each function.

关键词： Decision making Educational institutions evolutionary computation Insects Mathematical model Optimization Recruitment

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Multivariate Gaussian Copula in Estimation of Distribution Algorithm with Model Migration

Multivariate Gaussian Copula in Estimation of Distribution A...

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2014 IEEE Symposium on Foundations of computational Intelligence (FOCI)

作者： Hyrs, Martin Schwarz, Josef Brno Univ Technol Fac Informat Technol Bozetechova 1-2 Brno Czech Republic

ISBN: (纸本)9781479944910

The paper presents a new concept of an islandbased model of Estimation of Distribution Algorithms (EDAs) with a bidirectional topology in the field of numerical optimization in continuous domain. The traditional migration of individuals is replaced by the probability model migration. Instead of a classical joint probability distribution model, the multivariate Gaussian copula is used which must be specified by correlation coefficients and parameters of a univariate marginal distributions. The idea of the proposed Gaussian Copula EDA algorithm with model migration (GC-mEDA) is to modify the parameters of a resident model respective to each island by the immigrant model of the neighbour island. The performance of the proposed algorithm is tested over a group of five well-known benchmarks.

关键词： Gaussian distribution evolutionary computation optimisation topology GC-mEDA Gaussian copula EDA algorithm advanced evolutionary algorithms bidirectional topology classical joint probability distribution model continuous domain correlation coefficient distribution algorithm estimation immigrant model island-based model multivariate Gaussian copula neighbour island numerical optimization probability model migration resident model parameters univariate marginal distribution Correlation Distribution functions Joints Numerical models Probability distribution Standards Topology topology Distribution function evolutionary algorithm Probability distribution Gaussian distribution numerical models Allocation Algorithm Joints Joints correlation coefficient (r) Immigration Migrations Gauss

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Novel composition test functions for numerical global optimization

Novel composition test functions for numerical global optimi...

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IEEE Swarm Intelligence Symposium

作者： Liang, JJ Suganthan, PN Deb, K Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

ISBN: (纸本)0780389166

In the evolutionary optimization field, there exist some algorithms taking advantage of the known property of the benchmark functions, such as local optima lying along the coordinate axes, global optimum having the same values for many variables and so on. Multiagent genetic algorithm (MAGA) [1] is an example for this class of algorithms. In this paper, we identify shortcomings associated with the existing test functions. Novel hybrid benchmark functions whose complexity and properties can be controlled easily, are introduced and several evolutionary algorithms are evaluated with the novel test functions.

关键词： evolutionary computation optimisation benchmark functions composition test functions evolutionary algorithm evolutionary optimization local optima multiagent genetic algorithm numerical global optimization evolutionary algorithm Global optimization Genetic Algorithm (GA) coordinate axes algorithms local optimum BENCHMARKS

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Deterministic Crossover Based on Target Semantics in Geometric Semantic Genetic Programming 5

Deterministic Crossover Based on Target Semantics in Geometr...

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5th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI)

作者： Hara, Akira Kushida, Jun-ichi Tanemura, Ryo 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 Hiroshima Japan

ISBN: (纸本)9781467389853

In this paper, we focus on solving symbolic regression problems, in which we find functions approximating the relationships between given input and output data. Genetic Programming (GP) is often used for evolving tree structural numerical expressions. 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. In order to improve the search performance of GSGP, we propose an improved Geometric Semantic Crossover utilizing the information of the target semantics. In conventional GSGP, ratios of convex combinations are determined at random. On the other hand, our proposed method can use optimal ratios for affine combinations of parental individuals. We confirmed that our method showed better performance than conventional GSGP in several symbolic regression problems.

关键词： Genetic Programming evolutionary computation Symbolic Regression Geometric Semantic Crossover

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Real Time Traffic Intersection Management Using Multi-objective evolutionary Algorithm 5th

Real Time Traffic Intersection Management Using Multi-object...

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5th International Conference on the Theory and Practice of Natural Computing (TPNC)

作者： Ripon, Kazi Shah Nawaz Dissen, Hakon Solaas, Jostein Norwegian Univ Sci & Technol Dept Comp & Informat Sci Trondheim Norway

ISBN: (纸本)9783319490014;9783319490007

With the advent of autonomous vehicles, the field of traffic intersection management has changed. Most of the current methods for intersection management use either stochastic methods for optimizing single scheduling scenarios or deterministic algorithms to optimize parameters for intersection traffic lights. This paper proposes and explores the application of multi-objective evolutionary algorithm (MOEA) to manage a traffic intersection in real time. To achieve this goal, this work implements an intersection manager (IM) that divides the continuous problem into smaller discrete time steps. The vehicular behaviour in single time steps is then optimized, considering several optimization objectives with different goals in terms of overall performance.

关键词： evolutionary computation Real time traffic management Multi-objective optimization Intersection manager Discrete time steps

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