Evolutionary computing for routing and scheduling applications

Ph.DDOCTOR OF PHILOSOPH

Estudio e implementación de metaheurísticas para solucionar el problema de la selección deseada

Evolutionary algorithms are among the most successful approaches for solving a number of problems where systematic search in huge domains must be performed. One problem of practical interest that falls into this category is known as The Root Identification Problem in Geometric Constraint Solving, where one solution to the geometric problem must be selected among a number of possible solutions bounded by an exponential number. In this work we analize habilities and drawbacks of a series of metaheuristics in relation with the Root identification problem.Postprint (published version

Scheduling and Resource Efficiency Balancing. Discrete Species Conserving Cuckoo Search for Scheduling in an Uncertain Execution Environment

The main goal of a scheduling process is to decide when and how to execute each of the project’s activities. Despite large variety of researched scheduling problems, the majority of them can be described as generalisations of the resource-constrained project scheduling problem (RCPSP). Because of wide applicability and challenging difficulty, RCPSP has attracted vast amount of attention in the research community and great variety of heuristics have been adapted for solving it. Even though these heuristics are structurally different and operate according to diverse principles, they are designed to obtain only one solution at a time. In the recent researches on RCPSPs, it was proven that these kind of problems have complex multimodal fitness landscapes, which are characterised by a wide solution search spaces and presence of multiple local and global optima. The main goal of this thesis is twofold. Firstly, it presents a variation of the RCPSP that considers optimisation of projects in an uncertain environment where resources are modelled to adapt to their environment and, as the result of this, improve their efficiency. Secondly, modification of a novel evolutionary computation method Cuckoo Search (CS) is proposed, which has been adapted for solving combinatorial optimisation problems and modified to obtain multiple solutions. To test the proposed methodology, two sets of experiments are carried out. Firstly, the developed algorithm is applied to a real-life software development project. Secondly, the performance of the algorithm is tested on universal benchmark instances for scheduling problems which were modified to take into account specifics of the proposed optimisation model. The results of both experiments demonstrate that the proposed methodology achieves competitive level of performance and is capable of finding multiple global solutions, as well as prove its applicability in real-life projects

Structural optimization using evolutionary multimodal and bilevel optimization techniques

This research aims to investigate the multimodal properties of structural optimization using techniques from the field of evolutionary computation, specifically niching and bilevel techniques. Truss design is a well-known structural optimization problem which has important practical applications in many fields. Truss design problems are typically multimodal by nature, meaning that it offers multiple equally good design solutions with respect to the topology and/or sizes of the members, but they are evaluated to have similar or equally good objective function values. From a practical standpoint, it is desirable to find as many alternative designs as possible, rather than finding a single design, as often practiced. Niching is an intuitive way of finding multiple optimal solutions in a single optimization run. Literature shows that existing niching methods are largely designed for handling continuous optimization problems. There does not exist a well-studied niching method for constrained discrete optimization problems like truss design problems. In addition, there are no well-defined multimodal discrete benchmark problems that can be used to evaluate the reliability and robustness of such a niching method. This thesis fills the identified research gaps by means of five major contributions. In the first contribution, we design a test suite for producing a diverse set of challenging multimodal discrete benchmark problems, which can be used for evaluating the discrete niching methods. In the second contribution, we develop a binary speciation-based PSO (B-SPSO) niching method using the concept of speciation in nature along with the binary PSO (BPSO). The results show that the proposed multimodal discrete benchmark problems are useful for the evaluation of the discrete niching methods like B-SPSO. In light of this study, a time-varying transfer function based binary PSO (TVT-BPSO) is developed for the B-SPSO which is the third contribution of this thesis. We propose this TVT-BPSO for maintaining a better balance between exploration/exploitation during the search process of the BPSO. The results show that the TVT-BPSO outperforms the state-of-the-art discrete optimization methods on the large-scale 0-1 knapsack problems. The fourth contribution is to consider and formulate the truss design problem as a bilevel optimization problem. With this new formulation, truss topology can be optimized in the upper level, at the same time the size of that truss topology can be optimized in the lower level. The proposed bilevel formulation is a precursor to the development of a bilevel niching method (Bi-NM) which constitutes the fifth contribution of this thesis. The proposed Bi-NM method performs niching at the upper level and a local search at the lower level to further refine the solutions. Extensive empirical studies are carried out to examine the accuracy, robustness, and efficiency of the proposed bilevel niching method in finding multiple topologies and their size solutions. Our results confirm that the proposed bilevel niching method is superior in all these three aspects over the state-of-the-art methods on several low to high-dimensional truss design problems

Design and analysis of scalable rule induction systems

Machine learning has been studied intensively during the past two decades. One motivation has been the desire to automate the process of knowledge acquisition during the construction of expert systems. The recent emergence of data mining as a major application for machine learning algorithms has led to the need for algorithms that can handle very large data sets. In real data mining applications, data sets with millions of training examples, thousands of attributes and hundreds of classes are common. Designing learning algorithms appropriate for such applications has thus become an important research problem. A great deal of research in machine learning has focused on classification learning. Among the various machine learning approaches developed for classification, rule induction is of particular interest for data mining because it generates models in the form of IF-THEN rules which are more expressive and easier for humans to comprehend. One weakness with rule induction algorithms is that they often scale relatively poorly with large data sets, especially on noisy data. The work reported in this thesis aims to design and develop scalable rule induction algorithms that can process large data sets efficiently while building from them the best possible models. There are two main approaches for rule induction, represented respectively by CN2 and the AQ family of algorithms. These approaches vary in the search strategy employed for examining the space of possible rules, each of which has its own advantages and disadvantages. The first part of this thesis introduces a new rule induction algorithm for learning classification rules, which broadly follows the approach of algorithms represented by CN2. The algorithm presents a new search method which employs several novel search-space pruning rules and rule-evaluation techniques. This results in a highly efficient algorithm with improved induction performance. Real-world data do not only contain nominal attributes but also continuous attributes. The ability to handle continuously valued data is thus crucial to the success of any general purpose learning algorithm. Most current discretisation approaches are developed as pre- processes for learning algorithms. The second part of this thesis proposes a new approach which discretises continuous-valued attributes during the learning process. Incorporating discretisation into the learning process has the advantage of taking into account the bias inherent in the learning system as well as the interactions between the different attributes. This in turn leads to improved performance. Overfitting the training data is a major problem in machine learning, particularly when noise is present. Overfitting increases learning time and reduces both the accuracy and the comprehensibility of the generated rules, making learning from large data sets more difficult. Pruning is a technique widely used for addressing such problems and consequently forms an essential component of practical learning algorithms. The third part of this thesis presents three new pruning techniques for rule induction based on the Minimum Description Length (MDL) principle. The result is an effective learning algorithm that not only produces an accurate and compact rule set, but also significantly accelerates the learning process. RULES-3 Plus is a simple rule induction algorithm developed at the author's laboratory which follows a similar approach to the AQ family of algorithms. Despite having been successfully applied to many learning problems, it has some drawbacks which adversely affect its performance. The fourth part of this thesis reports on an attempt to overcome these drawbacks by utilising the ideas presented in the first three parts of the thesis. A new version of RULES-3 Plus is reported that is a general and efficient algorithm with a wide range of potential applications

Spatially-structured niching methods for evolutionary algorithms

Traditionally, an evolutionary algorithm (EA) operates on a single population with no restrictions on possible mating pairs. Interesting changes to the behaviour of EAs emerge when the structure of the population is altered so that mating between individuals is restricted. Variants of EAs that use such populations are grouped into the field of spatially-structured EAs (SSEAs). Previous research into the behaviour of SSEAs has primarily focused on the impact space has on the selection pressure in the system. Selection pressure is usually characterised by takeover times and the ratio between the neighbourhood size and the overall dimension of space. While this research has given indications into where and when the use of an SSEA might be suitable, it does not provide a complete coverage of system behaviour in SSEAs. This thesis presents new research into areas of SSEA behaviour that have been left either unexplored or briefly touched upon in current EA literature. The behaviour of genetic drift in finite panmictic populations is well understood. This thesis attempts to characterise the behaviour of genetic drift in spatially-structured populations. First, an empirical investigation into genetic drift in two commonly encountered topologies, rings and torii, is performed. An observation is made that genetic drift in these two configurations of space is independent of the genetic structure of individuals and additive of the equivalent-sized panmictic population. In addition, localised areas of homogeneity present themselves within the structure purely as a result of drifting. A model based on the theory of random walks to absorbing boundaries is presented which accurately characterises the time to fixation through random genetic drift in ring topologies. A large volume of research has gone into developing niching methods for solving multimodal problems. Previously, these techniques have used panmictic populations. This thesis introduces the concept of localised niching, where the typically global niching methods are applied to the overlapping demes of a spatially structured population. Two implementations, local sharing and local clearing are presented and are shown to be frequently faster and more robust to parameter settings, and applicable to more problems than their panmictic counterparts. Current SSEAs typically use a single fitness function across the entire population. In the context of multimodal problems, this means each location in space attempts to discover all the optima. A preferable situation would be to use the inherent spatial properties of an SSEA to localise optimisation of peaks. This thesis adapts concepts from multiobjective optimisation with environmental gradients and applies them to multimodal problems. In addition to adapting to the fitness landscape, individuals evolve towards their preferred environmental conditions. This has the effect of separating individuals into regions that concentrate on different optima with the global fitness function. The thesis also gives insights into the expected number of individuals occupying each optima in the problem. The SSEAs and related models developed in this thesis are of interest to both researchers and end-users of evolutionary computation. From the end-user’s perspective, the developed SSEAs require less a priori knowledge of a given problem domain in order to operate effectively, so they can be more readily applied to difficult, poorly-defined problems. Also, the theoretical findings of this thesis provides a more complete understanding of evolution within spatially-structured populations, which is of interest not only to evolutionary computation practitioners, but also to researchers in the fields of population genetics and ecology.UnpublishedAlba, E. and Dorronsoro, B. (2005). 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Schaffer (ed.), Proceedings of the Third International Conference on Genetic Algorithms (ICGA’89), Morgan Kaufmann, pp. 42–50. Dick, G. (2003a). An explicit spatial model for niching in genetic algorithms, in P. Whigham (ed.), The 15th Annual Colloquium of the Spatial Information Research Centre, pp. 151–157. Dick, G. (2003b). The spatially-dispersed genetic algorithm, in E. Cantú-Paz, J. A. Foster, K. Deb, D. Davis, R. Roy, U.-M. O’Reilly, H.-G. Beyer, R. Standish, G. Kendall, S. Wilson, M. Harman, J. Wegener, D. Dasgupta, M. A. Potter, A. C. Schultz, K. Dowsland, N. Jonoska and J. Miller (eds), Proceedings of the 2003 Conference on Genetic and Evolutionary Computation (GECCO 2003), Part II, Vol. 2724 of Lecture Notes in Computer Science, Springer, pp. 1572–1573. Dick, G. (2003c). The spatially-dispersed genetic algorithm: An explicit spatial population structure for GAs, in R. Sarker, R. Reynolds, H. Abbass, K. C. Tan, B. McKay, D. Essam and T. Gedeon (eds), Proceedings of the 2003 IEEE Congress on Evolutionary Computation, IEEE Press, pp. 2455–2461. Dick, G. (2004). An empirical investigation into correlation functions in a spatially-dispersed evolutionary algorithm, in P. Whigham (ed.), The 16th Annual Colloquium of the Spatial Information Research Centre, pp. 23–34. Dick, G. and Whigham, P. A. (2002). Spatially-constrained selection in evolutionary computation, in R. Sarker, R. McKay, M. Gen and A. Namatame (eds), The Sixth Australia-Japan Joint Workshop on Intelligent and Evolutionary Systems, pp. 93–100. Doebeli, M. and Dieckmann, U. (2003). Speciation along environmental gradients, Nature 421(6920): 259–264. Dongarra, J. (1994). MPI: A message passing interface standard, International Journal of Supercomputer Applications 8(3/4): 159–416. Ewens, W. J. (1963). The mean time for absorption in a process of genetic type, Journal of the Australian Mathematical Society 3: 375–383. Giacobini, M., Alba, E., Tettamanzi, A. and Tomassini, M. (2004). Modeling selection intensity for toroidal cellular evolutionary algorithms, in K. Deb, R. Poli, W. Banzhaf, H.-G. Beyer, E. Burke, P. Darwen, D. Dasgupta, D. Floreano, J. Foster, M. Harman, O. Holland, P. L. Lanzi, L. Spector, A. Tettamanzi, D. Thierens and A. Tyrrell (eds), Proceedings of the 2004 Conference on Genetic and Evolutionary Computation (GECCO 2004), Part I, Vol. 3102 of Lecture Notes in Computer Science, Springer, pp. 1138–1149. Giacobini, M., Alba, E. and Tomassini, M. (2003). Selection intensity in asynchronous cellular evolutionary algorithms, in E. Cantú-Paz, J. A. Foster, K. Deb, D. Davis, R. Roy, U.-M. O’Reilly, H.-G. Beyer, R. Standish, G. Kendall, S. Wilson, M. Harman, J. Wegener, D. Dasgupta, M. A. Potter, A. C. Schultz, K. Dowsland, N. Jonoska and J. 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Spatially-structured niching methods for evolutionary algorithms

Traditionally, an evolutionary algorithm (EA) operates on a single population with no restrictions on possible mating pairs. Interesting changes to the behaviour of EAs emerge when the structure of the population is altered so that mating between individuals is restricted. Variants of EAs that use such populations are grouped into the field of spatially-structured EAs (SSEAs). Previous research into the behaviour of SSEAs has primarily focused on the impact space has on the selection pressure in the system. Selection pressure is usually characterised by takeover times and the ratio between the neighbourhood size and the overall dimension of space. While this research has given indications into where and when the use of an SSEA might be suitable, it does not provide a complete coverage of system behaviour in SSEAs. This thesis presents new research into areas of SSEA behaviour that have been left either unexplored or briefly touched upon in current EA literature. The behaviour of genetic drift in finite panmictic populations is well understood. This thesis attempts to characterise the behaviour of genetic drift in spatially-structured populations. First, an empirical investigation into genetic drift in two commonly encountered topologies, rings and torii, is performed. An observation is made that genetic drift in these two configurations of space is independent of the genetic structure of individuals and additive of the equivalent-sized panmictic population. In addition, localised areas of homogeneity present themselves within the structure purely as a result of drifting. A model based on the theory of random walks to absorbing boundaries is presented which accurately characterises the time to fixation through random genetic drift in ring topologies. A large volume of research has gone into developing niching methods for solving multimodal problems. Previously, these techniques have used panmictic populations. This thesis introduces the concept of localised niching, where the typically global niching methods are applied to the overlapping demes of a spatially structured population. Two implementations, local sharing and local clearing are presented and are shown to be frequently faster and more robust to parameter settings, and applicable to more problems than their panmictic counterparts. Current SSEAs typically use a single fitness function across the entire population. In the context of multimodal problems, this means each location in space attempts to discover all the optima. A preferable situation would be to use the inherent spatial properties of an SSEA to localise optimisation of peaks. This thesis adapts concepts from multiobjective optimisation with environmental gradients and applies them to multimodal problems. In addition to adapting to the fitness landscape, individuals evolve towards their preferred environmental conditions. This has the effect of separating individuals into regions that concentrate on different optima with the global fitness function. The thesis also gives insights into the expected number of individuals occupying each optima in the problem. The SSEAs and related models developed in this thesis are of interest to both researchers and end-users of evolutionary computation. From the end-user’s perspective, the developed SSEAs require less a priori knowledge of a given problem domain in order to operate effectively, so they can be more readily applied to difficult, poorly-defined problems. Also, the theoretical findings of this thesis provides a more complete understanding of evolution within spatially-structured populations, which is of interest not only to evolutionary computation practitioners, but also to researchers in the fields of population genetics and ecology.UnpublishedAlba, E. and Dorronsoro, B. (2005). 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