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
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.
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.
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.
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.
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.
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.
13376924
Enumeration for spanning forests of complete bipartite graphs