16713800
On Evolutionary Optimization with Approximate Fitness Functions
The evaluation of the quality of solutions is usually very time-consuming in design optimization. Therefore, time-efficient approximate models can be particularly beneficial for the evaluation when evolutionary algorithms are applied. In this paper, the convergence property of an evolution strategy (ES) with neural network based fitness evaluations is investigated. It is found that the evolutionary algorithm will converge incorrectly if the approximate model has false optima. To address this problem, two strategies to control the evolution process are introduced. In addition, methods to eliminate false minima in neural network training are proposed. The effectiveness of the methods are shown with simulation studies on the Ackley function and the Rosenbrock function.
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.
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.
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.
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.
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.
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."
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.
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.
13376924
Enumeration for spanning forests of complete bipartite graphs