Search algorithms on structured and unstructured data in a large database

This project is concerned with the development of a search algorithm for a large archival database. The Port Elizabeth Genealogical Information System (PEGIS) contains a database consisting of almost 600000 individuals. The standard search algorithms are no longer sufficient to locate individuals in the database. A new algorithm was required that allows searches on any of the words or dates in the database, as well as a means to specify where in the desired record a word should occur. A ranking function of retrieved records was also required. A literature study on the field of Information Retrieval and on algorithms designed specifically for the PEGIS was done. These algorithms were adapted and hybridized to yield a search algorithm that allows for the boolean formulation of queries and the specification of the structure of search words in the desired records. The algorithm ranks retrieved records in assumed relevance to the user. The new algorithms were evaluated with regards to retrieval speed and accuracy and were found to be very effective

Adaptive multi-population differential evolution for dynamic environments

Dynamic optimisation problems are problems where the search space does not remain constant over time. Evolutionary algorithms aimed at static optimisation problems often fail to effectively optimise dynamic problems. The main reason for this is that the algorithms converge to a single optimum in the search space, and then lack the necessary diversity to locate new optima once the environment changes. Many approaches to adapting traditional evolutionary algorithms to dynamic environments are available in the literature, but differential evolution (DE) has been investigated as a base algorithm by only a few researchers. This thesis reports on adaptations of existing DE-based optimisation algorithms for dynamic environments. A novel approach, which evolves DE sub-populations based on performance in order to discover optima in an dynamic environment earlier, is proposed. It is shown that this approach reduces the average error in a wide range of benchmark instances. A second approach, which is shown to improve the location of individual optima in the search space, is combined with the first approach to form a new DE-based algorithm for dynamic optimisation problems. The algorithm is further adapted to dynamically spawn and remove sub-populations, which is shown to be an effective strategy on benchmark problems where the number of optima is unknown or fluctuates over time. Finally, approaches to self-adapting DE control parameters are incorporated into the newly created algorithms. Experimental evidence is presented to show that, apart from reducing the number of parameters to fine-tune, a benefit in terms of lower error values is found when employing self-adaptive control parameters.Thesis (PhD)--University of Pretoria, 2012.Computer Scienceunrestricte

Using competitive population evaluation in a differential evolution algorithm for dynamic environments

This paper proposes two adaptations to DynDE, a differential evolution-based algorithm for solving dynamic optimization problems. The first adapted algorithm, Competitive Population Evaluation (CPE), is a multi-population DE algorithm aimed at locating optima faster in the dynamic environment. This adaptation is based on allowing populations to compete for function evaluations based on their performance. The second adapted algorithm, Reinitialization Midpoint Check (RMC), is aimed at improving the technique used by DynDE to maintain populations on different peaks in the search space. A combination of the CPE and RMC adaptations is investigated. The new adaptations are empirically compared to DynDE using various problem sets. The empirical results show that the adaptations constitute an improvement over DynDE and compares favorably to other approaches in the literature. The general applicability of the adaptations is illustrated by incorporating the combination of CPE and RMC into another Differential Evolution-based algorithm, jDE, which is shown to yield improved results.http://www.elsevier.com/locate/ejo

Differential evolution for dynamic environments with unknown numbers of optima

This paper investigates optimization in dynamic environments where the numbers of optima are unknown or fluctuating. The authors present a novel algorithm, Dynamic Population DifferentialEvolution (DynPopDE),which is specifically designed for these problems. DynPopDE is a Differential Evolution based multi-population algorithm that dynamically spawns and removes populations as required. The new algorithm is evaluated on an extension of the Moving Peaks Benchmark. Comparisons with other state-of-the-art algorithms indicate that DynPopDE is an effective approach to use when the number of optima in a dynamic problem space is unknown or changing over time.http://link.springer.com/journal/10898hb201