18320669
Operator and parameter adaptation in genetic algorithms
Abstract Genetic Algorithms are a class of powerful, robust search techniques based on genetic inheritance and the Darwinian metaphor of “Natural Selection”. These algorithms maintain a finite memory of individual points on the search landscape known as the “population”. Members of the population are usually represented as strings written over some fixed alphabet, each of which has a scalar value attached to it reflecting its quality or “fitness”. The search may be seen as the iterative application of a number of operators, such as selection, recombination and mutation, to the population with the aim of producing progressively fitter individuals.These operators are usually static, that is to say that their mechanisms, parameters, and probability of application are fixed at the beginning and constant throughout the run of the algorithm. However, there is an increasing body of evidence that not only is there no single choice of operators which is optimal for all problems, but that in fact the optimal choice of operators for a given problem will be time-variant i.e. it will depend on such factors as the degree of convergence of the population. Based on theoretical and practical approaches, a number of authors have proposed methods of adaptively controlling one or more of the operators, usually invoking some kind of “meta-learning” algorithm, in order to try and improve the performance of the Genetic Algorithm as a function optimiser.In this paper we describe the background to these approaches, and suggest a framework for their classification, based on the learning strategy used to control them, and what facets of the algorithm are susceptible to adaptation. We then review a number of significant pieces of work within the context of this setting, and draw some conclusions about the relative merits of various approaches and promising directions for future work.
16182080
Self adaptation of mutation rates in a steady state genetic algorithm
This paper investigates the use of genetically encoded mutation rates within a "steady state" genetic algorithm in order to provide a self-adapting mutation mechanism for incremental evolution. One of the outcomes of this work will be a reduction in the number of parameters required to be set by the operator, thus facilitating the transfer of evolutionary computing techniques into an industrial setting. The NK family of landscapes is used to provide a variety of different problems with known statistical features in order to examine the effects of changing various parameters on the performance of the search. A number of policies are considered for the replacement of members of the population with newly created individuals and recombination of material between parents, and a number of methods of encoding for mutation rate are investigated. Empirical comparisons (using the "best-of current-population" metric) over a range of test problems show that a genetic algorithm incorporating the best "flavour" of the adaptive mutation operator outperformed the same algorithm when using any one of a variety of "standard" fixed mutation rates suggested by other authors.
3062981
Recombination strategy adaptation via evolution of gene linkage
Analyses recombination strategies resulting from evolving gene linkage on a variety of fitness landscapes with known properties. The evolution of "blocks" of linked genes within a gene-pool makes it possible for the recombination strategy to vary in both the amount of genetic material exchanged during the formation of a new individual and in the number of parents contributing genetic material. The strategies evolved are examined in the light of the known properties of the landscapes. The results obtained explain the previously recorded good performance of the algorithm as a self-adaptive mechanism for function optimisation, as the amount and type of recombination is able to adapt to suit the landscape being searched, unlike conventional "fixed" operators.
17447930
Incorporation of adaptive mutation based on subjective evaluation in an interactive evolution strategy
A rapidly emerging model in the field of adaptive computing is the symbiosis of human expertise with evolutionary algorithms for user controlled and directed search. The two aspects in any EA are the selection of individuals to reproduce based on some measure of their quality or fitness and the application of variation operators to produce new solutions. In the context of interactive evolution, these aspects are compounded by the need for rapid convergence to prevent user fatigue and to provide the user some control over the generation of new solutions. Elsewhere, in the work of the authors (2004), we have examined different policies for best incorporating the user into the evaluation and selection process. In this paper, we explore the hypothesis that user assigned fitness represents a source of information that can be used to control the variation process: effectively to broaden the search if none of the current solutions is promising, or focus the search and improve convergence speed in the vicinity of a good solution. The main aims of this study, therefore, are to analyse the advantages of using a user directed adaptive mutation strategy over fixed mutation step sizes in terms of time to converge and robustness of the resulting solution. We present results showing a qualitatively different type of search process can be obtained by using the user assigned fitness to control the nature of the mutation process. There is also a synergy between user-based selection and fitness-based mutation control which out performs either system on its own.
16664439
An alternative approach for the evaluation of the neocognitron
The neocognitron network is analysed from the point of view of the contribution of the di erent layers to the nal classi cation. A variation to the neocognitron which gives improved performance is suggested. This variant combines the low level feature extraction capabilities of the initial layers with alternative classi ers such as LVQ and Class Based Means Clustering. This is shown to give performance which is superior to the either of those classi ers acting on their own, and to the neocognitron in its standard form on two di erent instances of the letter recognition problem.
17502763
Alignment Of Protein Structures With A Memetic Evolutionary Algorithm
CATEGORY: Real-World Applications 
 
Structural comparison of proteins is a core problem in modern biomedical research. Identifying structural similarities is essential for the assessment of the relationship between structure and function in proteins, and structural comparison techniques play a key role in applications like rational drug design. In this paper we consider a technique for protein structure comparison known as the maximum contact map overlap problem. In this problem, the similarity between two protein structures is computed by aligning the proteins to maximize the number of shared contacts in their corresponding contact maps. 
 
We present a new approach to this problem that uses a Multimeme evolutionary algorithm. The best solution found by our algorithm provides a lower bound on the value of the optimal structural alignment between the proteins. We have evaluated the Multimeme algorithm on a range of benchmark problems and compared with previous heuristics. We apply a linear programming method, which provides an upper bound, to assess the accuracy of our solutions. Our experiments show that the Multimeme evolutionary algorithm represents a significant improvement on the current state of the art in metaheuristics for this problem.
15030600
Support for Distributed Pair Programming in the Transparent Video Facetop
The Transparent Video Facetop is a novel user interface concept that supports not only single-user interactions with a PC, but also close pair collaborations, such as that found in collaborative Web browsing, remote medicine, and in distributed pair programming. In this paper we discuss the use of a novel video-based UI called the Facetop [16] for solving several problems reported to us by teams doing distributed pair programming. Specifically, the Facetop allows a distributed pair to recapture some the facial expressions and face-to-face communications contact lost in earlier distributed sessions. It also allows members of a distributed pair to point conveniently, quickly, and naturally to their shared work, in the same manner (manually) that they do when seated side-by-side. Our results enhance the ability of organizations to do effective XP-style agile development with distributed teams.
8219620
SPIRE: a digital library for scientific information
Abstract.In this paper we describe the architecture and implementation of a digital library framework for scientific data, particularly imagery, with a focus on support for content-based search. Content is specified by the user at one or more of the following abstraction levels: pixel, feature, and semantic. An object-definition mechanism has been developed that supports example-based and constraint-based specification of both simple and complex query targets.This framework incorporates a methodology yielding a computationally efficient implementation of image processing algorithms, thus allowing the interactive, real-time extraction and manipulation of user-specified features and content during the execution of queries. The framework is well-suited for searching scientific databases, including satellite imagery, and medical and seismic data repositories, where the richness of the information does not allow the a priori generation of exhaustive indexes.
2339969
Interactive search fusion methods for video database retrieval
In this paper, we investigate a new method for video database retrieval using interactive search fusion. Recent video analysis techniques have enabled the extraction of a variety of descriptors of features, concepts, clusters, classification results, speech and textual terms, MPEG-7 metadata, and so on. However, given an information need users are faced with a daunting task of trying to formulate queries over these multiple disparate data sources in order to retrieve the desired video content. In this paper, we explore a novel approach based on search fusion in which the user interactively builds a query by sequentially choosing among the descriptors and data sources and by selecting from various combining and score aggregation functions to fuse results of individual searches. For example, the system allows building of queries such as "retrieve video clips that have color of beach scenes, the detection of sky, and detection of water". In this paper we present the search fusion method and evaluate the performance on a large video database.
17174080
A memetic algorithm to schedule planned maintenance for the national grid
The combination of local search operators, problem specific information and a genetic algorithm has provided very good results in certain scheduling problems, particularly in timetabling and maintenance scheduling problems. The resulting algorithm from this hybrid approach has been termed a Memetic Algorithm. This paper investigates the use of such an algorithm for the scheduling of transmission line maintenance for a known problem that has been addressed in the literature using a combination of a genetic algorithm and greedy optimisers.This problem is concerned with the scheduling of maintenance for an electricity transmission network where every transmission line must be maintained once within a specified time period. The objective is to avoid situations where sections of the network are disconnected, and to minimise the overloading of lines which are in service.In this paper we look at scheduling maintenance for the South Wales region of the national transmission network. We present and discuss, in some detail, a memetic algorithm that incorporates local search operators including tabu search and simulated annealing. A comparison is made both with the results from previous work, and against a selection of optimising techniques.The approach presented in this paper shows a significant improvement over previously published results on previously tackled problems. We also present results on another problem which has not been tackled in the literature but which is closer to the real world maintenance scheduling problems faced by such companies as The National Grid Company plc using the South Wales region.
84054
Adapting Multimedia Internet Content for Universal Access
Content delivery over the Internet needs to address both the multimedia nature of the content and the capabilities of the diverse client platforms the content is being delivered to. We present a system that adapts multimedia Web documents to optimally match the capabilities of the client device requesting it. This system has two key components. 1) A representation scheme called the InfoPyramid that provides a multimodal, multiresolution representation hierarchy for multimedia. 2) A customizer that selects the best content representation to meet the client capabilities while delivering the most value. We model the selection process as a resource allocation problem in a generalized rate distortion framework. In this framework, we address the issue of both multiple media types in a Web document and multiple resource types at the client. We extend this framework to allow prioritization on the content items in a Web document. We illustrate our content adaptation technique with a web server that adapts multimedia news stories to clients as diverse as workstations, PDA's and cellular phones.