15005988
Co-evolutionary modular neural networks for automatic problem decomposition
Decomposing a complex computational problem into sub-problems, which are computationally simpler to solve individually and which can be combined to produce a solution to the full problem, can efficiently lead to compact and general solutions. Modular neural networks represent one of the ways in which this divide-and-conquer strategy can be implemented. Here we present a co-evolutionary model which is used to design and optimize modular neural networks with task-specific modules. The model consists of two populations. The first population consists of a pool of modules and the second population synthesizes complete systems by drawing elements from the pool of modules. Modules represent a part of the solution, which co-operates with others in the module population to form a complete solution. With the help of two artificial supervised learning tasks created by mixing two sub-tasks we demonstrate that if a particular task decomposition is better in terms of performance on the overall task, it can be evolved using this co-evolutionary model.
11319753
Fitness Approximation In Evolutionary Computation - a Survey
Fitness evaluation is a basic operation in evolutionary computation. However, an explicit fitness function is not always available in real-world applications. In many cases, it is necessary to estimate the fitness function by constructing an approximate model. In this paper, a short survey on fitness approximation in evolutionary computation is given. Main issues like approximation models, model management schemes, learning methods as well as data sampling techniques are presented. Finally, interesting open problems are discussed.
15436633
Knowledge Incorporation into Neural Networks From Fuzzy Rules
The incorporation of prior knowledge into neural networks can improve neural network learning in several respects, for example, a faster learning speed and better generalization ability. However, neural network learning is data driven and there is no general way to exploit knowledge which is not in the form of data input-output pairs. In this paper, we propose two approaches for incorporating knowledge into neural networks from fuzzy rules. These fuzzy rules are generated based on expert knowledge or intuition. In the first approach, information from the derivative of the fuzzy system is used to regularize the neural network learning, whereas in the second approach the fuzzy rules are used as a catalyst. Simulation studies show that both approaches increase the learning speed significantly.
4516847
Incorporation Of Fuzzy Preferences Into Evolutionary Multiobjective Optimization
A method for incorporating fuzzy preferences into evolutionary multiobjective optimization is proposed. Fuzzy preferences are converted into interval-based weights instead of single-valued crisp weights. The weight intervals are combined with the evolutionary dynamic weighted aggregation to obtain the preferred Pareto-optimal solutions.
1006855
Prediction-Based Population Re-initialization for Evolutionary Dynamic Multi-objective Optimization
Optimization in changing environment is a challenging task, especially when multiple objectives are to be optimized simultaneously. The basic idea to address dynamic optimization problems is to utilize history information to guide future search. In this paper, two strategies for population re-initialization are introduced when a change in the environment is detected. The first strategy is to predict the new location of individuals from the location changes that have occurred in the history. The current population is then partially or completely replaced by the new individuals generated based on prediction. The second strategy is to perturb the current population with a Gaussian noise whose variance is estimated according to previous changes. The prediction based population re-initialization strategies, together with the random re-initialization method, are then compared on two bi-objective test problems. Conclusions on the different re-initialization strategies are drawn based on the preliminary empirical results.
9886529
Editorial to special issue on evolutionary computation in dynamic and uncertain environments
Many real-world optimization problems, e.g., hardware design and job shop scheduling, are subjected to dynamic and uncertain environments that are often impossible to avoid in practice. For instance, the fitness function is uncertain or noisy as a result of simulation/measurement errors or approximation errors. In addition, the design variables or environmental conditions may also perturb or change over time. For these dynamic and uncertain optimization problems, the objective of the evolutionary algorithm (EA) is no longer to simply locate the global optimum solution, but to continuously track the optimum in dynamic environments, or to find a robust solution that operates optimally in the presence of uncertainties. This poses serious challenges to conventional EAs. However, conventional EAs with proper enhancements are still good tools of choice for optimization problems in dynamic and uncertain environments. This is because EAs are basically inspired by principles of natural evolution, which takes place in the ever-changing dynamic and uncertain environments in nature. Handling dynamic and uncertain optimization problems in evolutionary computation has received increasing research interests over recent years due to its importance in practice. A variety of methods have been reported across a broad range of application backgrounds. This special issue aims to reflect the most recent advances, present sophisticated real-world applications, and explore future research directions in the field. Totally, we have received 14 submissions to this special issue, which cover a broad range of topics relevant to evolutionary computation in dynamic and uncertain environments. After a peer-review process, four papers of high quality are finally selected for publication in this special issue. These four papers represent the theoretical analysis, algorithm design, and application research in evolutionary computation under dynamic and uncertain environments. In the first paper, Hans-Georg Beyer and Silja Meyer-Nieberg investigate the selfadaptation behaviour of (1, λ)-evolution strategies (ES) on the noisy sphere model. The steady state behaviour of ES is analyzed theoretically for constant and scaled noise strength scenarios and compared to the real ES runs. An explanation is given for the random walk like behaviour of the mutation strength in the vicinity of the steady state. It is shown to be a peculiarity of the (1, λ)-ES, which does not affect intermediate recombination strategies. The second paper by Stefan Janson and Martin Middendorf addresses both noisy and dynamic environments using a partitioned hierarchical particle swarm optimization
16768615
The virtual design team
The long range goal of the Virtual Design Team" (VDT) research program is to develop computational tools to analyze decision making and communication behavior and thereby to support true organizational (re)engineering. This article introduces the underlying theory, the implementation of the theory as a computational model, and results from industrial test cases. Organization theory traditionally describes organizations only at an aggregate-level, describing and predicting the behavior of entire organizations in terms of general qualitative predictions. We define and implement a "micro" theory of the structure and behavior of components of organizations, explicitly representing activities, groups of people called "actors," and organizational structure and policies for project teams. A VDT model can be "run" by a discrete event simulation. Emergent aggregate model output behaviors include the predicted time to complete a project, the total effort to do the project, and a measure of process quality. More detailed model behaviors include the time-varying backlog of individual actors and the "exceptions" associated with activities. The results are detailed and specific, so they can guide specific managerial interventions in a project team and can support sensitivity studies of the relative impact of different organizational changes. We conclude that such a theory is tractable and predictive for complex but relatively routine, project-oriented design tasks. The application for which VDT offers unique new kinds of insights is where an organization is striving to shrink time to market dramatically for a product that is similar to ones it has previously developed. Reducing time to market dramatically almost always requires that previously sequential activities are executed more concurrently. In this situation, experienced managers can still correctly identify the required activities and estimate their durations and skill requirements; but they almost always underestimate the increased workload arising from exponentially higher coordination needs and the propagation of rework between the now highly concurrent activities. The VDT framework, which explicitly models information dependency and failure propagation between concurrent activities, has proven to be far more accurate, and to incorporate a wider range of parameters, than CPM/PERT process models for these fast-paced development projects."
16978884
2-D Shape Recognition using Recursive Landmark Determination and Fuzzy ART Network Learning
In this Letter, 2-D shape recognition is done using a combination of recursive search of landmarks, landmark-based invariant features, and a fuzzy ART neural-network classifier. To make this novel combination work well, an upper limit is imposed on the number of total landmarks allowed, and this maximum size is then translated into fixed dimensions of invariant features and into the neural processing of the features. It is shown that the recursive landmark search approximates very well any smooth 2-D shape contour, that the shape features used are independent of perspective transformation, and that, when combinedwitha fuzzy ART classifier, unknown features can be efficiently learned on-line to identify multiple distinct objects. An illustrative example is used to demonstrate effectiveness of the proposed algorithm.
6346685
Towards robust head tracking by particles
The paper presents a robust multi-feature head tracker using importance sampling particle filter with automatic head detection for video surveillance applications. Automatic head detection is based on moving region contour analysis using constraints of head shape and curvature scale space corner detector is introduced to segment contours. Due to the automatic head detection, we propose to exploit detected head shape cue to guide the sampling using importance sampling particle filter and individualized colour and edge features are fused for measurement. Experimental results demonstrate the robustness of the proposed head tracker.
10367277
OntoWebber: Model-Driven Ontology-Based Web Site Management
Building data-intensive Web sites and especially Web portals is a costly task, which requires considerable effort for data integration and maintenance and usually does not result in many reusable components. This is mainly because most of the design is hard-coded in static or dynamic Web pages. In this paper we integrate three different approaches to create a comprehensive solution to Web site and Web portal creation dubbed OntoWebber. OntoWebber integrates (1) the explicit modeling of different aspects of Web sites, (2) the use of ontologies as the foundation for Web portal design and (3) semi-structured data technology for data integration and Web site modeling. The resulting system and methodology supports the creation of reusable specifications of Web sites. OntoWebber is the basis for creating the Semantic Web Community Portal as part of the OntoAgents project, which will help the Semantic Web research community (distributed on the Web) to exchange and share knowledge conveniently and effectively.
13229821
Relational subscription middleware for Internet-scale publish-subscribe
We present a design of a distributed publish-subscribe system that extends the functionality of messaging middleware with "relational subscriptions", to support timely updates to state derived from published messages while preserving high throughput, scalability, and reliability.Critical to our design is our service guarantee of "eventual correctness". Eventual correctness is a weaker guarantee than the ACID properties of conventional databases, yet is useful enough to deliver state that is "just consistent enough".A key component of our design is a monotonic type system. All states delivered to clients represent facts that are permanently true, that may be refined by future updates, but will never become false. The monotonic type system is used both to formalize eventual correctness, and as a basis for our implementation, which generalizes the "Guaranteed Delivery" protocol previously implemented in the Gryphon system.We discuss: (1) our monotonic type system and relational subscription language; (2) eventual correctness; (3) the architecture of our implementation; (4) potential optimizations that form a basis for future studies.