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.
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.
4517361
Extracting Interpretable Fuzzy Rules from RBF Networks
Radial basis function networks and fuzzy rule systems are functionally equivalent under some mild conditions. Therefore, the learning algorithms developed in the field of artificial neural networks can be used to adapt the parameters of fuzzy systems. Unfortunately, after the neural network learning, the structure of the original fuzzy system is changed and interpretability, which is considered to be one of the most important features of fuzzy systems, is usually impaired. This Letter discusses the differences between RBF networks and interpretable fuzzy systems. Based on these discussions, a method for extracting interpretable fuzzy rules from RBF networks is suggested. Simulation examples are given to embody the idea of this paper.
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.
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
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
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.
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.
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.