17392905
Replacement Strategies in Steady State Genetic Algorithms: Static Environments
Recent years have seen increasing numbers of applications of Evolutionary Algorithms to non-stationary environments such as on-line process control. Studies have indicated that Genetic Algorithms using "Steady State" models demonstrate a greater ability to track moving optima than those using "Generational" models, however implementing the former requires an additional choice of which members of the current population should be replaced by new offspring. In this paper a number of selection and replacement strategies are compared for use in Steady State Genetic Algorithms working as function optimisers in dynamic environments. In addition to an algorithm with fixed mutation rates, the strategies are also compared in algorithms employing Cobb's Hypermutation method for tracking environmental changes. On-line and off-line metrics are used for comparison, which correspond to different types of real-world applications. In both cases it is shown that algorithms employing some kind of elitism outperform those that do not, which is related to previous studies on stationary environments. An investigation is made of various methods of implementing elitism, including an implicit method, "conservative" selection. It is shown that the latter, in addition to being computationally simpler, produces significantly better results on the problems used, and reasons are given for this behaviour.
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.
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.
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.
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.
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.
5110411
Quasigroups and quandles
Abstract Two connexions between quasigroups and quandles are established. In one direction, Joyce's homogeneous quandle construction is shown to yield a quasigroup isotopic to the loop constructed by Scimemi on the set of o-commutators of a group automorphism o. In the other direction, the universal multiplication group construction of quasigroup theory is extended to quandles. The group of a knot becomes the universal right multiplication group of the knot quandle.
1837954
Trace processors
Traces are dynamic instruction sequences constructed and cached by hardware. A microarchitecture organized around traces is presented as a means for efficiently executing many instructions per cycle. Trace processors exploit both control flow and data flow hierarchy to overcome complexity and architectural limitations of conventional superscalar processors by (1) distributing execution resources based on trace boundaries and (2) applying control and data prediction at the trace level rather than individual branches or instructions. Three sets of experiments using the SPECInt95 benchmarks are presented. (i) A detailed evaluation of trace processor configurations: the results affirm that significant instruction-level parallelism can be exploited in integer programs (2 to 6 instructions per cycle). We also isolate the impact of distributed resources, and quantify the value of successively doubling the number of distributed elements. (ii) A trace processor with data prediction applied to inter-trace dependences: potential performance improvement with perfect prediction is around 45% for all benchmarks. With realistic prediction, gcc achieves an actual improvement of 10%. (iii) Evaluation of aggressive control flow: some benchmarks benefit from control independence by as much as 10%.
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.
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.
31236386
Speeding Up Navigational Requests in a Parallel Object Database System
In data intensive applications, both programming and declarative query languages have attractions, the former in comprehensiveness and the latter for ease of use. Databases sometimes support the calling of side-effect free user defined functions from within declarative queries. As well as enabling more efficient coding of computationally intensive functions, this provision not only moves computation to data in a client-server setting, but also enables speedup through data parallel execution if the server is parallel. There has been little work on the combined use of query and program based database access in the context of parallel servers. We believe Polar is the first parallel object-oriented database which supports this arbitrary navigation both in a client application and in functions (operations) which may be called from within declarative queries. This work introduces Polar's support for navigation and for calling operations from within parallel queries and presents performance results for example navigational requests for the large OO7 benchmark.