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.
15458099
Theoretical comparisons of search dynamics of genetic algorithms and evolution strategies
Genetic algorithms (GAs) and evolution strategies (ESs) are two widely used evolutionary algorithms. The main differences between GAs and ESs lie in their representations and variation operators, which result in very different search dynamics. In this paper, we compare the search dynamics of GAs and ESs theoretically using a theoretical framework for analyzing the search dynamics of evolution strategies proposed in this paper and a framework for genetic algorithms we suggested in (2005). Based on the theoretical analysis, interesting aspects of the search dynamics of GAs and ESs for single objective optimization are revealed. As an extension, preliminary results on the search dynamics of GAs for multi-objective optimization are also presented
4474193
Evolutionary optimization in uncertain environments-a survey
Evolutionary algorithms often have to solve optimization problems in the presence of a wide range of uncertainties. Generally, uncertainties in evolutionary computation can be divided into the following four categories. First, the fitness function is noisy. Second, the design variables and/or the environmental parameters may change after optimization, and the quality of the obtained optimal solution should be robust against environmental changes or deviations from the optimal point. Third, the fitness function is approximated, which means that the fitness function suffers from approximation errors. Fourth, the optimum of the problem to be solved changes over time and, thus, the optimizer should be able to track the optimum continuously. In all these cases, additional measures must be taken so that evolutionary algorithms are still able to work satisfactorily. This paper attempts to provide a comprehensive overview of the related work within a unified framework, which has been scattered in a variety of research areas. Existing approaches to addressing different uncertainties are presented and discussed, and the relationship between the different categories of uncertainties are investigated. Finally, topics for future research are suggested.
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
5797189
Efficient Hierarchical Parallel Genetic Algorithms using Grid computing
In this paper, we present an efficient Hierarchical Parallel Genetic Algorithm framework using Grid computing (GE-HPGA). The framework is developed using standard Grid technologies, and has two distinctive features: (1) an extended GridRPC API to conceal the high complexity of the Grid environment, and (2) a metascheduler for seamless resource discovery and selection. To assess the practicality of the framework, a theoretical analysis of the possible speed-up offered is presented. An empirical study on GE-HPGA using a benchmark problem and a realistic aerodynamic airfoil shape optimization problem for diverse Grid environments having different communication protocols, cluster sizes, processing nodes, at geographically disparate locations also indicates that the proposed GE-HPGA using Grid computing offers a credible framework for providing a significant speed-up to evolutionary design optimization in science and engineering.
8331628
On Test Functions for Evolutionary Multi-objective Optimization
In order to evaluate the relative performance of optimization algorithms benchmark problems are frequently used. In the case of multi-objective optimization (MOO), we will show in this paper that most known benchmark problems belong to a constrained class of functions with piecewise linear Pareto fronts in the parameter space. We present a straightforward way to define benchmark problems with an arbitrary Pareto front both in the fitness and parameter spaces. Furthermore, we introduce a difficulty measure based on the mapping of probability density functions from parameter to fitness space. Finally, we evaluate two MOO algorithms for new benchmark problems.
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.
13376924
Enumeration for spanning forests of complete bipartite graphs
5139043
Representing Beliefs in the Fluent Calculus
Action formalisms like the fluent calculus have been developed to endow logic-based agents with the abilities to reason about the effects of actions, to execute high-level strategies, and to plan. In this paper we extend the fluent calculus by a method for belief change, which allows agents to revise their internal model upon making observations that contradict this model. Unlike the existing combination of the situation calculus with belief revision [16], our formalism satisfies all of the standard postulates for (iterated) belief change. Furthermore, we have extended the high-level action programming language FLUX by a computational approach to belief change which is provably equivalent to the axiomatic characterization in the fluent calculus.
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."
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.