Exploring and Exploiting Models of the Fitness Landscape: a Case Against Evolutionary Optimization

In recent years, the theories of natural selection and biological evolution have proved popular metaphors for understanding and solving optimization problems in engineering design. This thesis identifies some fundamental problems associated with this use of such metaphors. Key objections are the failure of evolutionary optimization techniques to represent explicitly the goal of the optimization process, and poor use of knowledge developed during the process. It is also suggested that convergent behaviour of an optimization algorithm is an undesirable quality if the algorithm is to be applied to multimodal problems. An alternative approach to optimization is suggested, based on the explicit use of knowledge and/or assumptions about the nature of the optimization problem to construct Bayesian probabilistic models of the surface being optimized and the goal of the optimization. Distinct exploratory and exploitative strategies are identified for carrying out optimization based on such models—exploration based on attempting to reduce maximally an entropy-based measure of the total uncertainty concerning the satisfaction of the optimization goal over the space, exploitation based on evalutation of the point judged most likely to achieve the goal—together with a composite strategy which combines exploration and exploitation in a principled manner. The behaviour of these strategies is empirically investigated on a number of test problems. Results suggest that the approach taken may well provide effective optimization in a way which addresses the criticisms made of the evolutionary metaphor, subject to issues of the computational cost of the approach being satisfactorily addressed

DEUM: a framework for an estimation of distribution algorithm based on Markov random fields.

Estimation of Distribution Algorithms (EDAs) belong to the class of population based optimisation algorithms. They are motivated by the idea of discovering and exploiting the interaction between variables in the solution. They estimate a probability distribution from population of solutions, and sample it to generate the next population. Many EDAs use probabilistic graphical modelling techniques for this purpose. In particular, directed graphical models (Bayesian networks) have been widely used in EDA. This thesis proposes an undirected graphical model (Markov Random Field (MRF)) approach to estimate and sample the distribution in EDAs. The interaction between variables in the solution is modelled as an undirected graph and the joint probability of a solution is factorised as a Gibbs distribution. The thesis describes a model of fitness function that approximates the energy in the Gibbs distribution, and shows how this model can be fitted to a population of solutions to estimate the parameters of the MRF. The estimated MRF is then sampled to generate the next population. This approach is applied to estimation of distribution in a general framework of an EDA, called Distribution Estimation using Markov Random Fields (DEUM). The thesis then proposes several variants of DEUM using different sampling techniques and tests their performance on a range of optimisation problems. The results show that, for most of the tested problems, the DEUM algorithms significantly outperform other EDAs, both in terms of number of fitness evaluations and the quality of the solutions found by them. There are two main explanations for the success of DEUM algorithms. Firstly, DEUM builds a model of fitness function to approximate the MRF. This contrasts with other EDAs, which build a model of selected solutions. This allows DEUM to use fitness in variation part of the evolution. Secondly, DEUM exploits the temperature coefficient in the Gibbs distribution to regulate the behaviour of the algorithm. In particular, with higher temperature, the distribution is closer to being uniform and with lower temperature it concentrates near some global optima. This gives DEUM an explicit control over the convergence of the algorithm, resulting in better optimisation

Swarm intelligence optimization of the group method of data handling using the cuckoo search and whale optimization algorithms to model and predict landslides

The robustness of landslide prediction models has become a major focus of researchers worldwide. We developed two novel hybrid predictive models that combine the self-organizing, deep-learning group method of data handling (GMDH) with two swarm intelligence optimization algorithms, i.e., cuckoo search algorithm (CSA) and whale optimization algorithm (WOA) for spatially explicit prediction of landslide susceptibility. Eleven landslide-causing factors and 334 historic landslides in a 31,340 km2 landslide-prone area in Iran were used to produce geospatial training and validation datasets. The GMDH model was employed to develop a basic predictive model that was then restructured and its parameters were optimized using the CSA and WOA algorithms, yielding the novel hybrid GMDH-CSA and GMDH-WOA models. The hybrid models were validated and compared to the standalone GMDH model by calculating the area under the receiver operating characteristic (AUC) curve and root mean square error (RMSE). The results demonstrated that the hybrid models overcame the computational shortcomings of the basic GMDH model and significantly improved landslide susceptibility prediction (GMDH-CSA, AUC = 0.909 and RMSE = 0.089; GMDH-WOA, AUC = 0.902 and RMSE = 0.129; standalone GMDH, AUC = 0.791 and RMSE = 0.226). Further, the hybrid models were more robust than the standalone GMDH model, showing consistently excellent performance when the training and validation datasets were changed. Overall, the swarm intelligence-optimized models, but not the standalone model, identified the best trade-offs among objectives, accuracy, and robustness

Prescriptive formalism for constructing domain-specific evolutionary algorithms

It has been widely recognised in the computational intelligence and machine learning communities that the key to understanding the behaviour of learning algorithms is to understand what representation is employed to capture and manipulate knowledge acquired during the learning process. However, traditional evolutionary algorithms have tended to employ a fixed representation space (binary strings), in order to allow the use of standardised genetic operators. This approach leads to complications for many problem domains, as it forces a somewhat artificial mapping between the problem variables and the canonical binary representation, especially when there are dependencies between problem variables (e.g. problems naturally defined over permutations). This often obscures the relationship between genetic structure and problem features, making it difficult to understand the actions of the standard genetic operators with reference to problem-specific structures. This thesis instead advocates m..

Analysis and modeling a distributed co-operative multi agent system for scaling-up business intelligence

Modeling A Distributed Co-Operative Multi Agent System in the area of Business Intelligence is the newer topic. During the work carried out a software Integrated Intelligent Advisory Model (IIAM) has been develop, which is a personal finance portfolio ma

A multiparental cross population for mapping QTL for relevant agronomic traits in durum wheat (Triticum durum Desf.)

Multiparental cross designs for mapping quantitative trait loci (QTL) in crops are efficient alternatives to conventional biparental experimental populations because they exploit a broader genetic basis and higher mapping resolution. We describe the development and deployment of a multiparental recombinant inbred line (RIL) population in durum wheat (Triticum durum Desf.) obtained by crossing four elite cultivars characterized by different traits of agronomic value. A linkage map spanning 2,663 cM and including 7,594 single nucleotide polymorphisms (SNPs) was produced by genotyping 338 RILs with a wheat-dedicated 90k SNP chip. A cluster file was developed for correct allele calling in the framework of the tetraploid durum wheat genome. Based on phenotypic data collected over four field experiments, a multi-trait quantitative trait loci (QTL) analysis was carried out for 18 traits of agronomic relevance (including yield, yield-components, morpho-physiological and seed quality traits). Across environments, a total of 63 QTL were identified and characterized in terms of the four founder haplotypes. We mapped two QTL for grain yield across environments and 23 QTL for grain yield components. A novel major QTL for number of grain per spikelet/ear was mapped on chr 2A and shown to control up to 39% of phenotypic variance in this cross. Functionally different QTL alleles, in terms of direction and size of genetic effect, were distributed among the four parents. Based on the occurrence of QTL-clusters, we characterized the breeding values (in terms of effects on yield) of most of QTL for heading and maturity as well as yield component and quality QTL. This multiparental RIL population provides the wheat community with a highly informative QTL mapping resource enabling the dissection of the genetic architecture of multiple agronomic relevant traits in durum wheat

An investigation of Monte Carlo tree search and local search for course timetabling problems

The work presented in this thesis focuses on solving course timetabling problems, a variant of education timetabling. Automated timetabling is a popular topic among researchers and practitioners because manual timetable construction is impractical, if not impossible, as it is known to be NP-hard. A two-stage approach is investigated. The first stage involves finding feasible solutions. Monte Carlo Tree Search (MCTS) is utilized in this stage. As far as we are aware, it is used for the first time in addressing the timetabling problem. It is a relatively new search method and has achieved breakthrough in the domain of games particularly Go. Several enhancements are attempted on MCTS such as heuristic based simulations and pruning. We also compare the effectiveness of MCTS with Graph Coloring Heuristic (GCH) and Tabu Search (TS) based methods. Initial findings show that a TS based method is more promising, so we focus on improving TS. We propose an algorithm called Tabu Search with Sampling and Perturbation (TSSP). Among the enhancements that we introduced are event sampling, a novel cost function and perturbation. Furthermore, we hybridize TSSP with Iterated Local Search (ILS). The second stage focuses on improving the quality of feasible solutions. We propose a variant of Simulated Annealing called Simulated Annealing with Reheating (SAR). SAR has three features: a novel neighborhood examination scheme, a new way of estimating local optima and a reheating scheme. The rigorous setting of initial and end temperature in conventional SA is bypassed in SAR. Precisely, reheating and cooling were applied at the right time and level, thus saving time allowing the search to be performed efficiently. One drawback of SAR is having to preset the composition of neighborhood structures for the datasets. We present an enhanced variant of the SAR algorithm called Simulated Annealing with Improved Reheating and Learning (SAIRL). We propose a reinforcement learning based method to obtain a suitable neighborhood structure composition for the search to operate effectively. We also propose to incorporate the average cost changes into the reheated temperature function. SAIRL eliminates the need for tuning parameters in conventional SA as well as neighborhood structures composition in SAR. Experiments were tested on four publicly available datasets namely Socha, International Timetabling Competition 2002 (ITC02), International Timetabling Competition 2007 (ITC07) and Hard. Our results are better or competitive when compared with other state of the art methods where new best results are obtained for many instances

Knowledge-based marine conservation in oil spill risk management

Maritime transport is an efficient way to ferry goods, oil, and chemicals but shipping poses a threat to marine ecosystems. Oil spills have a potential to extinguish or debilitate fish and wildlife populations and habitat types important to the marine ecosystem. In this thesis, I study the resources and methods for collecting data and knowledge about the adverse impacts of oil on sensitive species and habitat types. Furthermore, I study how ecological knowledge could be passed to decision-makers and how the risks should be communicated. Finally, I discuss future policy improvements and scientific needs for ecological knowledge in oil spill risk management. This forms a synthesis of what kind of ecological information is required for the environmental risk management and conservation of marine ecosystems under oil spill threat. The thesis includes five papers, where we develop methods to assess the environmental impacts of oil spills and the effectiveness of management practices to mitigate their adverse ecological effects. Improved strategies combine theoretical disciplines, such as population biology with practical oil spill response. The results demonstrate that environmental risk assessment models can be used to structure problems, integrate knowledge and uncertainty, and persuade decision-makers by visualizing the results. Since the objective of risk assessment is to synthesize information for environmental management and policy design, which should rely on the extensive use of scientific evidence, communication between academia and decision-makers is of great importance. The use of Bayesian networks would improve the current oil spill risk management in the Baltic Sea, since all the variables affecting oil spill risk can be presented in one framework in a transparent manner. Many geospatial services work as tools of informative policy instruments, as they deliver ecological data and knowledge for oil spill risk management. Researchers could also participate more often in the contingency planning or practical management of oil spills as experts. Thus, all the relevant knowledge could be integrated into the decision-making process. This thesis offers new insights into oil spill risk management in the Baltic Sea and provides examples showing how evidence-based management actions should be chosen and carried out in order to minimize the risks. Policy recommendations are also provided. First, in oil spill risk management, the marine ecosystem should be prioritized based on its conservation value, recovery potential and protection effectiveness. Second, because preventive measures against oil accidents are considered cost-effective, maritime safety should be increased, with stricter and regional ship inspection practices. The effects of policy innovations should be assessed using probabilistic policy-support tools.Rannikon luonto tulee priorisoida sen suojeluarvon, öljyn aiheuttaman tappion, lajien ja luontotyyppien palautumiskyvyn ja suojattavuuden perusteella. Meriliikenne on tehokas tapa kuljettaa tavaroita, kemikaaleja ja öljyä. Merenkulku kuitenkin uhkaa meriekosysteemejä, sillä alusonnettomuuksista aiheutuvat öljyonnettomuudet voivat heikentää tai hävittää lajeja ja niiden elinympäristöjä. Karttapohjaiset sovellukset voivat toimia neuvoa-antavina työkaluina suojelutoimien kohdistamisessa. Väitöskirja arvioi öljyonnettomuuksien aiheuttamia haitallisia vaikutuksia Itämeren luontoon ja tarkastelee millaisilla toimintamalleilla niitä voidaan pienentää. Työn viisi osajulkaisua pyrkivät vastaamaan kysymyksiin siitä, millaisin menetelmin öljyonnettomuuksien haittoja voidaan tutkia, miten tieto saaduista tuloksista voidaan siirtää päätöksentekoon ja kuinka riskinarviointia kannattaa kehittää. Väitöskirjatutkimuksessa on erityisesti arvioitu rannikon uhanalaisten lajien ja luontotyyppien palautumiskykyä ja suunniteltu työkaluja riskinhallintaan. Tulokset osoittavat, että todennäköisyyspohjaisten verkkomallien avulla tehty riskinarviointi voi auttaa jäsentämään ongelmia, huomioimaan epävarmuutta ja vakuuttamaan päätöksentekijöitä. Öljyonnettomuusriskien hallintaan tulee luoda uusi viestintäkulttuuri, jossa tutkijat voivat osallistua entistä enemmän torjunnan suunnitteluun. Tämä edistää merkittävästi tietoon perustuvaa päätöksentekoa. Onnettomuusriskiä tulee myös hallita entistä tiukemmilla ja paikallisilla keinoilla. Itämeri on ainutlaatuinen ekosysteemi. Sen pohjoinen sijainti, pieni vesitilavuus, hidas veden vaihtuvuus ja pohjan hapettomuus tekevät alueesta herkän öljyn vaikutuksille. Suomen rannikon mataluus ja saariston rikkonaisuus lisäävät öljyn haitallisia vaikutuksia: matala vesisyvyys lisää eliöiden altistumistodennäköisyyttä ja öljyn pitoisuutta meressä, ja rannikon muoto tekee siitä haasteellisen öljyntorjunnalle. Itämeressä monet lajit elävät sopeutumisensa äärirajoilla ja ovat siksi herkkiä ympäristömuutoksille kuten öljyonnettomuuksille. Kansainvälinen merenkulkujärjestö onkin nimennyt Itämeren erityisen herkäksi merialueeksi

An investigation of Monte Carlo tree search and local search for course timetabling problems

The work presented in this thesis focuses on solving course timetabling problems, a variant of education timetabling. Automated timetabling is a popular topic among researchers and practitioners because manual timetable construction is impractical, if not impossible, as it is known to be NP-hard. A two-stage approach is investigated. The first stage involves finding feasible solutions. Monte Carlo Tree Search (MCTS) is utilized in this stage. As far as we are aware, it is used for the first time in addressing the timetabling problem. It is a relatively new search method and has achieved breakthrough in the domain of games particularly Go. Several enhancements are attempted on MCTS such as heuristic based simulations and pruning. We also compare the effectiveness of MCTS with Graph Coloring Heuristic (GCH) and Tabu Search (TS) based methods. Initial findings show that a TS based method is more promising, so we focus on improving TS. We propose an algorithm called Tabu Search with Sampling and Perturbation (TSSP). Among the enhancements that we introduced are event sampling, a novel cost function and perturbation. Furthermore, we hybridize TSSP with Iterated Local Search (ILS). The second stage focuses on improving the quality of feasible solutions. We propose a variant of Simulated Annealing called Simulated Annealing with Reheating (SAR). SAR has three features: a novel neighborhood examination scheme, a new way of estimating local optima and a reheating scheme. The rigorous setting of initial and end temperature in conventional SA is bypassed in SAR. Precisely, reheating and cooling were applied at the right time and level, thus saving time allowing the search to be performed efficiently. One drawback of SAR is having to preset the composition of neighborhood structures for the datasets. We present an enhanced variant of the SAR algorithm called Simulated Annealing with Improved Reheating and Learning (SAIRL). We propose a reinforcement learning based method to obtain a suitable neighborhood structure composition for the search to operate effectively. We also propose to incorporate the average cost changes into the reheated temperature function. SAIRL eliminates the need for tuning parameters in conventional SA as well as neighborhood structures composition in SAR. Experiments were tested on four publicly available datasets namely Socha, International Timetabling Competition 2002 (ITC02), International Timetabling Competition 2007 (ITC07) and Hard. Our results are better or competitive when compared with other state of the art methods where new best results are obtained for many instances