Dependency structure matrix, genetic algorithms, and effective recombination

In many different fields, researchers are often confronted by problems arising from complex systems. Simple heuristics or even enumeration works quite well on small and easy problems; however, to efficiently solve large and difficult problems, proper decomposition is the key. In this paper, investigating and analyzing interactions between components of complex systems shed some light on problem decomposition. By recognizing three bare-bones interactions-modularity, hierarchy, and overlap, facet-wise models arc developed to dissect and inspect problem decomposition in the context of genetic algorithms. The proposed genetic algorithm design utilizes a matrix representation of an interaction graph to analyze and explicitly decompose the problem. The results from this paper should benefit research both technically and scientifically. Technically, this paper develops an automated dependency structure matrix clustering technique and utilizes it to design a model-building genetic algorithm that learns and delivers the problem structure. Scientifically, the explicit interaction model describes the problem structure very well and helps researchers gain important insights through the explicitness of the procedure.This work was sponsored by Taiwan National Science Council under grant NSC97- 2218-E-002-020-MY3, U.S. Air Force Office of Scientific Research, Air Force Material Command, USAF, under grants FA9550-06-1-0370 and FA9550-06-1-0096, U.S. National Science Foundation under CAREER grant ECS-0547013, ITR grant DMR-03-25939 at Materials Computation Center, grant ISS-02-09199 at US National Center for Supercomputing Applications, UIUC, and the Portuguese Foundation for Science and Technology under grants SFRH/BD/16980/2004 and PTDC/EIA/67776/2006

Medium-Voltage Distribution Network Expansion Planning with Gene-pool Optimal Mixing Evolutionary Algorithms

Medium-voltage distribution network expansion planning involves finding the most economical adjustments of both the capacity and the topology of the network such that no operational constraints are violated and the expected loads, that the expansion is planned for, can be supplied. This paper tackles this important real-world problem using realistic yet computationally feasible models and, for the first time, using two instances of the recently proposed class of Gene-pool Optimal Mixing Evolutionary Algorithms (GOMEAs) that have previously been shown to be a highly efficient integration of local search and genetic recombination, but only on standard benchmark problems. One GOMEA instance that we use employs linkage learning and one instance assumes no dependencies among problem variables. We also conduct experiments with a widely used traditional Genetic Algorithm (GA). Our results show that the favorable performance of GOMEA instances over traditional GAs extends to the real-world problem at hand. Moreover, the use of linkage learning is shown to further increase the algorithm's effectiveness in converging toward optimal solutions

Adaptive scaling of evolvable systems

Neo-Darwinian evolution is an established natural inspiration for computational optimisation with a diverse range of forms. A particular feature of models such as Genetic Algorithms (GA) [18, 12] is the incremental combination of partial solutions distributed within a population of solutions. This mechanism in principle allows certain problems to be solved which would not be amenable to a simple local search. Such problems require these partial solutions, generally known as building-blocks, to be handled without disruption. The traditional means for this is a combination of a suitable chromosome ordering with a sympathetic recombination operator. More advanced algorithms attempt to adapt to accommodate these dependencies during the search. The recent approach of Estimation of Distribution Algorithms (EDA) aims to directly infer a probabilistic model of a promising population distribution from a sample of fitter solutions [23]. This model is then sampled to generate a new solution set. A symbiotic view of evolution is behind the recent development of the Compositional Search Evolutionary Algorithms (CSEA) [49, 19, 8] which build up an incremental model of variable dependencies conditional on a series of tests. Building-blocks are retained as explicit genetic structures and conditionally joined to form higher-order structures. These have been shown to be effective on special classes of hierarchical problems but are unproven on less tightly-structured problems. We propose that there exists a simple yet powerful combination of the above approaches: the persistent, adapting dependency model of a compositional pool with the expressive and compact variable weighting of probabilistic models. We review and deconstruct some of the key methods above for the purpose of determining their individual drawbacks and their common principles. By this reasoned approach we aim to arrive at a unifying framework that can adaptively scale to span a range of problem structure classes. This is implemented in a novel algorithm called the Transitional Evolutionary Algorithm (TEA). This is empirically validated in an incremental manner, verifying the various facets of the TEA and comparing it with related algorithms for an increasingly structured series of benchmark problems. This prompts some refinements to result in a simple and general algorithm that is nevertheless competitive with state-of-the-art methods

Substructural local search in discrete estimation of distribution algorithms

Tese dout., Engenharia Electrónica e Computação, Universidade do Algarve, 2009SFRH/BD/16980/2004The last decade has seen the rise and consolidation of a new trend of stochastic optimizers known as estimation of distribution algorithms (EDAs). In essence, EDAs build probabilistic models of promising solutions and sample from the corresponding probability distributions to obtain new solutions. This approach has brought a new view to evolutionary computation because, while solving a given problem with an EDA, the user has access to a set of models that reveal probabilistic dependencies between variables, an important source of information about the problem. This dissertation proposes the integration of substructural local search (SLS) in EDAs to speedup the convergence to optimal solutions. Substructural neighborhoods are de ned by the structure of the probabilistic models used in EDAs, generating adaptive neighborhoods capable of automatic discovery and exploitation of problem regularities. Speci cally, the thesis focuses on the extended compact genetic algorithm and the Bayesian optimization algorithm. The utility of SLS in EDAs is investigated for a number of boundedly di cult problems with modularity, overlapping, and hierarchy, while considering important aspects such as scaling and noise. The results show that SLS can substantially reduce the number of function evaluations required to solve some of these problems. More importantly, the speedups obtained can scale up to the square root of the problem size O( p `).Fundação para a Ciência e Tecnologia (FCT

A population study of the minicircles in Trypanosoma cruzi: predicting guide RNAs in the absence of empirical RNA editing

<p>Abstract</p> <p>Background</p> <p>The structurally complex network of minicircles and maxicircles comprising the mitochondrial DNA of kinetoplastids mirrors the complexity of the RNA editing process that is required for faithful expression of encrypted maxicircle genes. Although a few of the guide RNAs that direct this editing process have been discovered on maxicircles, guide RNAs are mostly found on the minicircles. The nuclear and maxicircle genomes have been sequenced and assembled for <it>Trypanosoma cruzi</it>, the causative agent of Chagas disease, however the complement of 1.4-kb minicircles, carrying four guide RNA genes per molecule in this parasite, has been less thoroughly characterised.</p> <p>Results</p> <p>Fifty-four CL Brener and 53 Esmeraldo strain minicircle sequence reads were extracted from <it>T. cruzi </it>whole genome shotgun sequencing data. With these sequences and all published <it>T. cruzi </it>minicircle sequences, 108 unique guide RNAs from all known <it>T. cruzi </it>minicircle sequences and two guide RNAs from the CL Brener maxicircle were predicted using a local alignment algorithm and mapped onto predicted or experimentally determined sequences of edited maxicircle open reading frames. For half of the sequences no statistically significant guide RNA could be assigned. Likely positions of these unidentified gRNAs in <it>T. cruzi </it>minicircle sequences are estimated using a simple Hidden Markov Model. With the local alignment predictions as a standard, the HMM had an ~85% chance of correctly identifying at least 20 nucleotides of guide RNA from a given minicircle sequence. Inter-minicircle recombination was documented. Variable regions contain species-specific areas of distinct nucleotide preference. Two maxicircle guide RNA genes were found.</p> <p>Conclusion</p> <p>The identification of new minicircle sequences and the further characterization of all published minicircles are presented, including the first observation of recombination between minicircles. Extrapolation suggests a level of 4% recombinants in the population, supporting a relatively high recombination rate that may serve to minimize the persistence of gRNA pseudogenes. Characteristic nucleotide preferences observed within variable regions provide potential clues regarding the transcription and maturation of <it>T. cruzi </it>guide RNAs. Based on these preferences, a method of predicting <it>T. cruzi </it>guide RNAs using only primary minicircle sequence data was created.</p

Robust evolutionary algorithms

Evolutionary Algorithms (EAs) have shown great potential to solve complex real world problems, but their dependence on problem specific configuration in order to obtain high quality performance prevents EAs from achieving widespread use. While it is widely accepted that statically configuring an EA is already a complex problem, dynamic configuration of an EA is a combinatorially harder problem. Evidence provided here supports the claim that EAs achieve the best results when using dynamic configurations. By designing methods that automatically configure parts of an EA or by changing how EAs work to avoid configurable aspects, EAs can be made more robust, allowing them better performance on a wider variety of problems with less requirements on the user. Two methods are presented in this thesis to increase the robustness of EAs. The first is a novel algorithm designed to automatically configure and dynamically update the recombination method which is used by the EA to exploit known information to create new solutions. The techniques used by this algorithm can likely be applied to other aspects of an EA in the future, leading to even more robust EAs. The second is an existing set of algorithms which only require a single configurable parameter. The analysis of the existing set led to the creation of a new variation, as well as a better understanding of how these algorithms work. Both methods are able to outperform more traditional EAs while also making both easier to apply to new problems. By building upon these methods, and perhaps combining them, EAs can become even more robust and become more widely used --Abstract, page iv