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
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.
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
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.
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.
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.
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.
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.
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
26853065
Support for Integrated Value-Based Maintenance Planning
The paper considers how IRTMM intelligent real-time maintenance management system helps process-plant engineers and owners perform value-based plant maintenance. With IRTMM, they can inspect subsystems, identify component operating parameters, and review and make notes regarding component performance or operational and maintenance history.