2766632
On Appropriate Adaptation Levels for the Learning of Gene Linkage
A number of algorithms have been proposed aimed at tackling the problem of learning “Gene Linkage” within the context of genetic optimisation, that is to say, the problem of learning which groups of co-adapted genes should be inherited together during the recombination process. These may be seen within a wider context as a search for appropriate relations which delineate the search space and “guide” heuristic optimisation, or, alternatively, as a part of a comprehensive body of work into Adaptive Evolutionary Algorithms.In this paper, we consider the learning of Gene Linkage as an emergent property of adaptive recombination operators. This is in contrast to the behaviour observed with fixed recombination strategies in which there is no correspondence between the sets of genes which are inherited together between generations, other than that caused by distributional bias. A discrete mathematical model of Gene Linkage is introduced, and the common families of recombination operators, along with some well known linkage-learning algorithms, are modelled within this framework. This model naturally leads to the specification of a recombination operator that explicitly operates on sets of linked genes.Variants of that algorithm, are then used to examine one of the important concepts from the study of adaptivity in Evolutionary Algorithms, namely that of the level (population, individual, or component) at which learning takes place. This is an aspect of adaptation which has received considerable attention when applied to mutation operators, but which has been paid little attention in the context of adaptive recombination operators and linkage learning. It is shown that even with the problem restricted to learning adjacent linkage, the population based variants are not capable of correctly identifying building blocks. This is in contrast to component level adaptation which outperforms conventional operators whose bias is ideal for the problems considered.
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.
796697
A tutorial for competent memetic algorithms: model, taxonomy, and design issues
The combination of evolutionary algorithms with local search was named "memetic algorithms" (MAs) (Moscato, 1989). These methods are inspired by models of natural systems that combine the evolutionary adaptation of a population with individual learning within the lifetimes of its members. Additionally, MAs are inspired by Richard Dawkin's concept of a meme, which represents a unit of cultural evolution that can exhibit local refinement (Dawkins, 1976). In the case of MA's, "memes" refer to the strategies (e.g., local refinement, perturbation, or constructive methods, etc.) that are employed to improve individuals. In this paper, we review some works on the application of MAs to well-known combinatorial optimization problems, and place them in a framework defined by a general syntactic model. This model provides us with a classification scheme based on a computable index D, which facilitates algorithmic comparisons and suggests areas for future research. Also, by having an abstract model for this class of metaheuristics, it is possible to explore their design space and better understand their behavior from a theoretical standpoint. We illustrate the theoretical and practical relevance of this model and taxonomy for MAs in the context of a discussion of important design issues that must be addressed to produce effective and efficient MAs.
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.
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.
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.
16384039
Experimenting with object navigation in parallel object databases
This paper addresses the problem of selecting the best object navigation strategy for a query to be run in parallel, when considering multiple alternative strategies. This is done by performing experiments with three pointer-based join algorithms, running simple and complex queries in a parallel ODMG-compliant object database system. We believe our results provide insights into how physical properties of queries impact on the performance of different joins operators in a parallel environment.
10698762
Exact and heuristic algorithms for solving the generalized minimum filter placement problem
We consider a problem of placing route-based filters in a communication network to limit the number of forged address attacks to a prescribed level. Nodes in the network communicate by exchanging packets along arcs, and the originating node embeds the origin and destination addresses within each packet that it sends. In the absence of a validation mechanism, one node can send packets to another node using a forged origin address to launch an attack against that node. Route-based filters can be established at various nodes on the communication network to protect against these attacks. A route-based filter examines each packet arriving at a node, and determines whether or not the origin address could be legitimate, based on the arc on which the packet arrives, the routing information, and possibly the destination. The problem we consider seeks to find a minimum cardinality subset of nodes to filter so that the prescribed level of security is achieved. We formulate a mixed-integer programming model for the problem and derive valid inequalities for this model by identifying polynomially-solvable subgraphs of the communication network. We also present three heuristics for solving the filter placement problem and evaluate their performance against the optimal solution provided by the mixed-integer programming model.
568831
Evolving fuzzy decision tree structure that adapts in real-time
A fuzzy logic algorithm has been developed that automatically allocates electronic attack (EA) resources distributed over different platforms in real-time. The controller must be able to make decisions based on rules provided by experts. The fuzzy logic approach allows the direct incorporation of expertise. Genetic algorithm based optimization is conducted to determine the form of the membership functions for the fuzzy root concepts. The resource manager is made up of five parts, the isolated platform model, the multi-platform model, the fuzzy EA model, the fuzzy parameter selection tree and the fuzzy strategy tree. Automatic determination of fuzzy decision tree topology using a genetic program, an algorithm that creates other algorithms is discussed. A comparison to subtrees obtained using a genetic program and those constructed by hand from rules is made. Experiments designed to test various concepts in the expert system are discussed, including its ability to allow multiple platforms to self-organize.
1805433
MPEG-7 MDS Content Description Tools and Applications
In this paper, we present the tools specified by the MDS part of the MPEG-7 standard for describing multimedia data such as images and video. In particular, we focus on the description tools that represent the structure and semantics of multimedia data to whose development we have actively contributed. We also describe some of our research prototype systems dealing with the extraction and application of MPEG-7 structural and semantic descriptions. These systems are AMOS, a video object segmentation and retrieval system, and IMKA, an intelligent multimedia knowledge application using the MediaNet knowledge representation framework.
9479207
Ontology-based multi-classification learning for video concept detection
In this paper, an ontology-based multi-classification learning algorithm is adopted to detect concepts in the NIST TREC-2003 video retrieval benchmark which defines 133 video concepts, organized hierarchically and each video data can belong to one or more concepts. The algorithm consists of two steps. In the first step, each single concept model is constructed independently. In the second step, ontology-based concept learning improves the accuracy of the individual concept by considering the possible influence relations between concepts based on a predefined ontology hierarchy. The advantage of ontology learning is that its influence path is based on an ontology hierarchy, which has real semantic meanings. Besides semantics, it also considers the data correlation to decide the exact influence assigned to each path, which makes the influence more flexible according to data distribution. This learning algorithm can be used for multiple topic document classification such as Internet documents and video documents. We demonstrate that precision-recall can be significantly improved by taking ontology into account