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检索条件"主题词=evolutionary computation"

共 14935 条记录，以下是4871-4880 订阅

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E-Means: An evolutionary Clustering Algorithm

引用

3rd International Conference on Intelligence computation and Applications

作者： Lu, Wei Tong, Hengjian Traore, Issa Univ New Brunswick Fac Comp Sci Fredericton NB E3B 5A3 Canada China Univ Geosci Sch Comp Sci Beijing Peoples R China Victoria Univ Dept Elect & Comp Engn Victoria BC Canada

ISBN: (纸本)9783540921363

In this paper we propose a new evolutionary clustering algorithm named E-means. E-means is an evolutionary extension of k-means algorithm that is composed by a revised k-means algorithm and an evolutionary approach to Gaussian mixture model, which estimates automatically the number of clusters and the optimal mean for each cluster. More specifically, the proposed E-means algorithm defines an entropy-based fitness function, and three genetic operators for merging, initiation, and deletion components. We conduct two sets of experiments using a synthetic dataset and an existing benchmark to validate the proposed E-means algorithm. The results obtained in the first experiment show that the algorithm can estimate exactly the optimal number of Clusters for a set of data. In the second experiment. we compute nine major clustering validity indices and compare the corresponding results With those obtained using four established Clustering techniques, and found that our E-means algorithm achieves better clustering structures.

关键词： evolutionary Clustering K-means evolutionary computation Gaussian Mixture Model Clustering Validity

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Investigation and Comparison of Inertia Weight Control Schemes in Particle Swarm Optimization 15

Investigation and Comparison of Inertia Weight Control Schem...

引用

15th International Conference on Advanced computational Intelligence (ICACI)

作者： Xu, Xin-Xin Gong, Hui -Li Ding, Xiang-Qian Ocean Univ China Sch Comp Sci & Technol Qingdao 266100 Shandong Peoples R China Ocean Univ China Internet Res Inst Qingdao Shandong Peoples R China

ISBN: (纸本)9798350321456

Particle swarm optimization (PSO) is a global search optimization algorithm that simulates the swarm behavior of birds. Compared with other evolutionary computation algorithms, PSO is simple, easy to implement, and has powerful optimization ability. However, the performance of PSO is heavily affected by the inertial weight parameter. Many studies have shown that PSO variant with smaller inertial weight has good local search ability and can improve the solution accuracy;while the PSO variant with larger inertial weight has good global search ability and can avoid falling into local optimum to a certain extent. Therefore, many researchers have proposed a variety of control schemes for dynamically adjusting the inertia weight. This paper systematically introduces, analyzes, and compares five typical decreasing and random control schemes in the literatures for dynamically adjusting the inertia weight. Moreover, a decreasing scheme based on concave exponential function, and two incremental schemes based on concave exponential function and convex exponential function are also designed and compared. The investigations and comparisons are conducted on 10 typical different unimodal and multimodal functions. The experimental investigations and comparisons are helpful for researchers to adopt different kind of inertia weight control schemes to solve different kinds of optimization problems.

关键词： Particle swarm optimization (PSO) inertia weight exponential decreasing exponential increasing evolutionary computation control scheme

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Evolution strategies for multivariate-to-anything partially specified random vector generation

Evolution strategies for multivariate-to-anything partially ...

引用

Congress on evolutionary computation (CEC 2004)

作者： Stanhope, S Univ Wisconsin Dept Stat Madison WI 53703 USA

ISBN: (纸本)0780385152

Multivariate-to-anything methods for partially specified random vector generation work by transforming samples from a driving distribution into samples characterized by given marginals and correlations. The correlations of the transformed random vector are controlled by the driving distribution;sampling a partially specified random vector requires finding an appropriate driving distribution. This paper motivates the use of evolution strategies for solving such problems and compares evolution strategies to conjugate gradient methods in the context of solving a Dirichlet-to-anything transformation. It is shown that the evolution strategy is at least as effective as the conjugate gradient method for solution of the parameterization problem.

关键词： evolutionary computation conjugate gradient methods random number generation evolution strategies multivariate-to-anything random vector generation partially specified random vector generation partially specified random vector sampling driving distribution conjugate gradient methods Dirichlet-to-anything transformation parameterization problem

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evolutionary Programming For Automatic Generation of Military Strategies in RTS Games

Evolutionary Programming For Automatic Generation of Militar...

引用

13th International Conference on Computer Games (CGames 2008)

作者： Huat, Ch'ng Siong Teo, Jason Daut, Norhayati Univ Malaysia Sabah Ctr Artificial Intelligence Sch Informat Technol & Engn Kota Kinabalu Sabah Malaysia

ISBN: (纸本)9780954901660

evolutionary Algorithms (EAs) are commonly used for finding optimal solutions in large and difficult search spaces. In real-time strategy (RTS) games, huge search spaces in addition to the highly dynamic environments constitute tremendous challenges for the generation of game artificial intelligence (AI), yet there have been very few attempts to date that use EAs to optimize game AI in RTS games. This paper describes an experiment regarding how RTS military strategies can be optimized using a branch of EAs known as evolutionary Programming (EP). To reduce the effect of dynamic noise arising from the highly stochastic RTS playing environment, a (mu+mu) EP survivor selection strategy is applied. The results from the experiment clearly show that firstly, the problem is non-trivial and more importantly, the (mu+mu) EP methodology is able to automatically generate good military strategies and significantly outperforms the control setup of using randomly generated strategies. Moreover, the results also show that the evolutionary optimization approach could learn very fast and can thus be practically used as an online game AI evolutionary method against human players, which can then make the game more challenging and interesting for the human players.

关键词： Game AI real-time strategy evolutionary computation dynamic optimization

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Evolving Explicit Opponent Models in Game Playing 07

Evolving Explicit Opponent Models in Game Playing

引用

Annual Conference of Genetic and evolutionary computation Conference

作者： Lockett, Alan J. Chen, Charles L. Miikkulainen, Risto Univ Texas Austin Dept Comp Sci Austin TX 78712 USA

ISBN: (纸本)9781595936974

Opponent models are necessary in games where the game state is only partially known to the player, since the player must infer the state of the game based on the opponent's actions. This paper presents an architecture and a process for developing neural network game players that utilize explicit opponent models in order to improve game play against unseen opponents. The model is constructed as a mixture over a set of cardinal opponents, i.e. opponents that represent maximally distinct game strategies. The model is trained to estimate the likelihood that the opponent will make the same move as each of the cardinal opponents would in a given game situation. Experiments were performed in the game of Guess It, a simple game of imperfect information that has no optimal strategy for defeating specific opponents. Opponent modeling is therefore crucial to play this game well. Both opponent modeling and game-playing neural networks were trained using NeuroEvolution of Augmenting Topologies (NEAT). The results demonstrate that game-playing provided with the model outperform networks not provided with the model when played against the same previously unseen opponents. The "cardinal mixture" architecture therefore constitutes a promising approach for general and dynamic opponent modeling in game-playing.

关键词： Opponent modeling neuroevolution neural networks games of imperfect information AI artificial intelligence evolutionary computation

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Improving Performance of CDCL SAT Solvers by Automated Design of Variable Selection Heuristics

Improving Performance of CDCL SAT Solvers by Automated Desig...

引用

IEEE Symposium Series on computational Intelligence (IEEE SSCI)

作者： Illetskova, Marketa Bertels, Alex R. Tuggle, Joshua M. Harter, Adam Richter, Samuel Tauritz, Daniel R. Mulder, Samuel Bueno, Denis Leger, Michelle Siever, William M. Missouri Univ Sci & Technol Dept Comp Sci Rolla MO 65409 USA Sandia Natl Labs POB 5800 Albuquerque NM 87185 USA Washington Univ Dept Comp Sci & Engn St Louis MO USA

ISBN: (纸本)9781538627266

Many real-world engineering and science problems can be mapped to Boolean satisfiability problems (SAT). CDCL SAT solvers are among the most efficient solvers. Previous work showed that instances derived from a particular problem class exhibit a unique underlying structure which impacts the effectiveness of a solver's variable selection scheme. Thus, customizing the variable scoring heuristic of a solver to a particular problem class can significantly enhance the solver's performance;however, manually performing such customization is very labor intensive. This paper presents a system for automating the design of variable scoring heuristics for CDCL solvers, making it feasible to tailor solvers to arbitrary problem classes. Experimental results are provided demonstrating that this system, which evolves variable scoring heuristics using an asynchronous parallel hyper-heuristics approach employing genetic programming, has the potential to create more efficient solvers for particular problem classes.

关键词： Sociology Statistics Runtime Portfolios Input variables evolutionary computation Genetics

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Hybrid ACO/EA Algorithms applied to the Multi-Agent Patrolling Problem

Hybrid ACO/EA Algorithms applied to the Multi-Agent Patrolli...

引用

IEEE Congress on evolutionary computation (CEC)

作者： Lauri, Fabrice Koukam, Abderrafiaa IRTES SeT F-90010 Belfort France

ISBN: (纸本)9781479914883

Patrolling an environment consists in visiting as frequently as possible its most relevant areas in order to supervise, control or protect it. This task is commonly performed by a team of agents that need to coordinate their actions for achieving optimal performance. We address here the problem of multi-agent patrolling in known environments where agents may move at different speeds and visit priorities on some areas may be specified. Two classes of patrolling strategies are studied: the single-cycle strategies and the partition-based strategies. Several single-core and multi-core variants of a template state-of-the-art hybrid algorithm are proposed for generating partition-based strategies. These are experimentally compared with a state-of-the-art heuristic-based algorithm generating single-cycle strategies. Experimental results show that: the heuristic-based algorithm only generates efficient strategies when agents move at the same speeds and no visit priorities have been defined;all single-core variants are equivalent;multi-core hybrid algorithms may improve overall quality or reduce variance of the solutions obtained by single-core algorithms.

关键词： Electronic mail evolutionary computation Heuristic algorithms Nickel Partitioning algorithms Sociology Statistics

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The Body is Not a Given: Joint Agent Policy Learning and Morphology Evolution 18

The Body is Not a Given: Joint Agent Policy Learning and Mor...

引用

18th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS)

作者： Banarse, Dylan Bachrach, Yoram Liu, Siqi Lever, Guy Heess, Nicolas Fernando, Chrisantha Kohli, Pushmeet Graepel, Thore DeepMind London England

ISBN: (纸本)9781450363099

Reinforcement learning (RL) has proven to be a powerful paradigm for deriving complex behaviors from simple reward signals in a wide range of environments. When applying RL to continuous control agents in simulated physics environments, the body is usually considered to be part of the environment. However, during evolution the physical body of biological organisms and their controlling brains are co -evolved, thus exploring a much larger space of actuator/controller configurations. Put differently, the intelligence does not reside only in the agent's mind, but also in the design of their body. We propose a method for uncovering strong agents, consisting of a good combination of a body and policy, based on combining RL with an evolutionary procedure. Given the resulting agent, we also propose an approach for identifying the body changes that contributed the most to the agent performance. We use the Shapley value from cooperative game theory to find the fair contribution of individual components, taking into account synergies between components. We evaluate our methods in an environment similar to the the recently proposed Robo-Sumo task, where agents in a software physics simulator compete in tipping over their opponent or pushing them out of the arena. Our results show that the proposed methods are indeed capable of generating strong agents, significantly outperforming baselines that focus on optimizing the agent policy alone. A video is available at: https://***/CH1ecRim9PI

关键词： Reinforcement Learning evolutionary computation

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A theoretical analysis of the HIFF problem 05

A theoretical analysis of the HIFF problem

引用

Genetic and evolutionary computation Conference

作者： McPhee, Nicholas Freitag Crane, Ellery Fussell Univ Minnesota Div Sci & Math Minneapolis MN 55455 USA

ISBN: (纸本)1595930108

We present a theoretical analysis of Watson's Hierarchical-if-and-only-if (HIFF) problem using a variety of tools. These include schema theory and course graining, the concept of effective fitness, and statistical analysis. We first review the use of Stephen's exact schema equations and schema basis to compute the changes in population distributions over time. We then use the tools described above to solve for the limit distributions of the 2 and 4-bit HIFF problems, and show that these limit distributions are essentially one-dimensional. We also show that a combination of fitness and the number of break points (a rough measure of distance in crossover space) in a string can be used to almost completely explain the limit distribution in the 4-bit HIFF problem.

关键词： theory evolutionary computation schema theory hierarchical-if-and-only-if (HIFF) problem limit distributions distance metrics

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Guided evolutionary Search for Boolean Networks in the Density Classification Problem 15th

Guided Evolutionary Search for Boolean Networks in the Densi...

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15th International Conference on Distributed Computing and Artificial Intelligence (DCAI)

作者： de Mattos, Thiago de Oliveira, Pedro P. B. Univ Presbiteriana Mackenzie PPGEEC Sao Paulo SP Brazil Univ Presbiteriana Mackenzie FCI Sao Paulo SP Brazil

ISBN: (纸本)9783319946498;9783319946481

Boolean networks consist of nodes that represent binary variables, which are computed as a function of the values represented by their adjacent nodes. This local processing entails global behaviors, such as the convergence to fixed points, a behavior found in the context of the density classification problem, where the aim is the network's convergence to a fixed point of the prevailing node value in the initial global configuration of the network;in other words, a global decision is targeted, but according to a constrained, non-global action. Here, we rely on evolutionary searches in order to find rules and network topologies with good performance in the task. All nodes' neighborhoods are assumed to be defined by non-regular and bidirectional links, and the Boolean function of the network initialized by the local majority rule. Two evolutionary searches are carried out: first, in the space of network topologies, guided by a parameter (omega) related to the 'small-worldness' of the networks, and then, in the space of Boolean functions, but constraining the network topologies to the best family identified in the previous experiment. The results clearly make it evident the key and successful role of the. parameter in looking for solutions to the task at issue.

关键词： Boolean networks Cellular automata Density classification evolutionary computation

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