Self-Adaptive Genetic Algorithms with Simulated Binary Crossover

Self-adaptation is an essential feature of natural evolution. However, in the context of function optimization, self-adaptation features of evolutionary search algorithms have been explored only with evolution strategy (ES) and evolutionary programming (EP). In this paper, we demonstrate the self-adaptive feature of real-parameter genetic algorithms (GAs) using simulated binary crossover (SBX) operator and without any mutation operator. The connection between the working of self-adaptive ESs and real-parameter GAs with SBX operator is also discussed. Thereafter, the self-adaptive behavior of real-parameter GAs is demonstrated on a number of test problems commonly-used in the ES literature. The remarkable similarity in the working principle of real-parameter GAs and self-adaptive ESs shown in this study suggests the need of emphasizing further studies on self-adaptive GAs

Bayesian phylogenetic modelling of lateral gene transfers

PhD ThesisPhylogenetic trees represent the evolutionary relationships between a set of species. Inferring these trees from data is particularly challenging sometimes since the transfer of genetic material can occur not only from parents to their o spring but also between organisms via lateral gene transfers (LGTs). Thus, the presence of LGTs means that genes in a genome can each have di erent evolutionary histories, represented by di erent gene trees. A few statistical approaches have been introduced to explore non-vertical evolution through collections of Markov-dependent gene trees. In 2005 Suchard described a Bayesian hierarchical model for joint inference of gene trees and an underlying species tree, where a layer in the model linked gene trees to the species tree via a sequence of unknown lateral gene transfers. In his model LGT was modeled via a random walk in the tree space derived from the subtree prune and regraft (SPR) operator on unrooted trees. However, the use of SPR moves to represent LGT in an unrooted tree is problematic, since the transference of DNA between two organisms implies the contemporaneity of both organisms and therefore it can allow unrealistic LGTs. This thesis describes a related hierarchical Bayesian phylogenetic model for reconstructing phylogenetic trees which imposes a temporal constraint on LGTs, namely that they can only occur between species which exist concurrently. This is achieved by taking into account possible time orderings of divergence events in trees, without explicitly modelling divergence times. An extended version of the SPR operator is introduced as a more adequate mechanism to represent the LGT e ect in a tree. The extended SPR operation respects the time ordering. It additionaly di ers from regular SPR as it maintains a 1-to-1 correspondence between points on the species tree and points on each gene tree. Each point on a gene tree represents the existence of a population containing that gene at some point in time. Hierarchical phylogenetic models were used in the reconstruction of each gene tree from its corresponding gene alignment, enabling the pooling of information across genes. In addition to Suchard's approach, we assume variation in the rate of evolution between di erent sites. The species tree is assumed to be xed. A Markov Chain Monte Carlo (MCMC) algorithm was developed to t the model in a Bayesian framework. A novel MCMC proposal mechanism for jointly proposing the gene tree topology and branch lengths, LGT distance and LGT history has been developed as well as a novel graphical tool to represent LGT history, the LGT Biplot. Our model was applied to simulated and experimental datasets. More speci cally we analysed LGT/reassortment presence in the evolution of 2009 Swine-Origin In uenza Type A virus. Future improvements of our model and algorithm should include joint inference of the species tree, improving the computational e ciency of the MCMC algorithm and better consideration of other factors that can cause discordance of gene trees and species trees such as gene loss

Evolutionary Decomposition of Complex Design Spaces

This dissertation investigates the support of conceptual engineering design through the decomposition of multi-dimensional search spaces into regions of high performance. Such decomposition helps the designer identify optimal design directions by the elimination of infeasible or undesirable regions within the search space. Moreover, high levels of interaction between the designer and the model increases overall domain knowledge and significantly reduces uncertainty relating to the design task at hand. The aim of the research is to develop the archetypal Cluster Oriented Genetic Algorithm (COGA) which achieves search space decomposition by using variable mutation (vmCOGA) to promote diverse search and an Adaptive Filter (AF) to extract solutions of high performance [Parmee 1996a, 1996b]. Since COGAs are primarily used to decompose design domains of unknown nature within a real-time environment, the elimination of apriori knowledge, speed and robustness are paramount. Furthermore COGA should promote the in-depth exploration of the entire search space, sampling all optima and the surrounding areas. Finally any proposed system should allow for trouble free integration within a Graphical User Interface environment. The replacement of the variable mutation strategy with a number of algorithms which increase search space sampling are investigated. Utility is then increased by incorporating a control mechanism that maintains optimal performance by adapting each algorithm throughout search by means of a feedback measure based upon population convergence. Robustness is greatly improved by modifying the Adaptive Filter through the introduction of a process that ensures more accurate modelling of the evolving population. The performance of each prospective algorithm is assessed upon a suite of two-dimensional test functions using a set of novel performance metrics. A six dimensional test function is also developed where the areas of high performance are explicitly known, thus allowing for evaluation under conditions of increased dimensionality. Further complexity is introduced by two real world models described by both continuous and discrete parameters. These relate to the design of conceptual airframes and cooling hole geometries within a gas turbine. Results are promising and indicate significant improvement over the vmCOGA in terms of all desired criteria. This further supports the utilisation of COGA as a decision support tool during the conceptual phase of design.British Aerospace plc, Warton and Rolls Royce plc, Filto

Optimal reliability-based design of bulk water supply infrastructure-incorporating pumping systems

The optimal design of a bulk water supply system is centered on two major objectives: cost efficiency and the formation of a design solution that is appropriate for the conditions in which the system is to be implemented. The currently employed CSIR (2000) design guidelines utilise deterministic measures to size system components. The efficiency of following a deterministic approach to bulk water system design, involving pumping systems, was investigated. This was seen as necessary owing to the vast spectrum of influences and the interrelation of parameters that constitute a bulk water supply system. A model developed by Chang & van Zyl (2012) sought to address this inefficiency by optimizing a bulk water supply system, with the core objectives of cost and reliability. The determination of these objectives was achieved by using a capital cost model for cost determination and a stochastic model developed by Van Zyl et al. (2008) for reliability. While this produced workable results, the application was relatively limited, and applied only to non-pumped, gravity-fed flow. As such, the failure mechanisms of the supply system did not include the effects of pump failure, an important influence on overall system reliability. In addition, the costing system was based solely on capital cost and did not take into account the life-cycle cost involved with the implementation of a bulk water supply system. The investigation sought to expand the applicability of the model through the incorporation of pumping systems and life-cycle costing. It was further intended to compare the expanded model to both the model developed by Chang & van Zyl (2012) and the CSIR (2000) guidelines. A sensitivity analysis would also be performed

Optimal reliability-based design of bulk water supply systems

Includes bibliographical references.Bulk water supply systems are usually designed according to deterministic design guidelines. In South Africa, design guidelines specify that a bulk storage reservoir should have a storage capacity of 48 hours of annual average daily demand (AADD), and the feeder pipe a capacity of 1.5 times AADD (CSIR, 2000). Nel & Haarhoff (1996) proposed a stochastic analysis method that allowed the reliability of a reservoir to be estimated based on a Monte Carlo analysis of consumer demand, fire water demand and pipe failures. Van Zyl et al. (2008) developed this method further and proposed a design criterion of one failure in ten years under seasonal peak conditions. In this study, a method for the optimal design of bulk water supply systems is proposed with the design variables being the configuration of the feeder pipe system, the feeder pipe diameters (i.e. capacity), and the size of the bulk storage reservoir. The stochastic analysis method is applied to determine a trade-off curve between system cost and reliability, from which the designer can select a suitable solution. Optimisation of the bulk system was performed using the multi-objective genetic algorithm, NSGA-II. As Monte Carlo sampling can be computationally expensive, especially when large numbers of simulations are required in an optimisation exercise, a compression heuristic was implemented and refined to reduce the computational effort required of the stochastic simulation. Use of the compression heuristic instead of full Monte Carlo simulation in the reliability analysis achieved computational time savings of around 75% for the optimisation of a typical system. Application of the optimisation model showed that it was able to successfully produce a set of Pareto-optimal solutions ranging from low reliability, low cost solutions to high reliability, high cost solutions. The proposed method was first applied to a typical system, resulting in an optimal reservoir size of approximately 22 h AADD and feeder pipe capacity of 2 times AADD. This solution achieved 9% savings in total system cost compared to the South African design guidelines. In addition, the optimal solution proved to have better reliability that one designed according to South African guidelines. A sensitivity analysis demonstrated the effects of changing various system and stochastic parameters from typical to low and high values. The sensitivity results revealed that the length of the feeder pipe system has the greatest impact on both the cost and reliability of the bulk system. It was also found that a single feeder pipe is optimal in most cases, and that parallel feeder pipes are only optimal for short feeder pipe lengths. The optimisation model is capable of narrowing down the search region to a handful of possible design solutions, and can thus be used by the engineer as a tool to assist with the design of the final system

Investigating hybrids of evolution and learning for real-parameter optimization

In recent years, more and more advanced techniques have been developed in the field of hybridizing of evolution and learning, this means that more applications with these techniques can benefit from this progress. One example of these advanced techniques is the Learnable Evolution Model (LEM), which adopts learning as a guide for the general evolutionary search. Despite this trend and the progress in LEM, there are still many ideas and attempts which deserve further investigations and tests. For this purpose, this thesis has developed a number of new algorithms attempting to combine more learning algorithms with evolution in different ways. With these developments, we expect to understand the effects and relations between evolution and learning, and also achieve better performances in solving complex problems. The machine learning algorithms combined into the standard Genetic Algorithm (GA) are the supervised learning method k-nearest-neighbors (KNN), the Entropy-Based Discretization (ED) method, and the decision tree learning algorithm ID3. We test these algorithms on various real-parameter function optimization problems, especially the functions in the special session on CEC 2005 real-parameter function optimization. Additionally, a medical cancer chemotherapy treatment problem is solved in this thesis by some of our hybrid algorithms. The performances of these algorithms are compared with standard genetic algorithms and other well-known contemporary evolution and learning hybrid algorithms. Some of them are the CovarianceMatrix Adaptation Evolution Strategies (CMAES), and variants of the Estimation of Distribution Algorithms (EDA). Some important results have been derived from our experiments on these developed algorithms. Among them, we found that even some very simple learning methods hybridized properly with evolution procedure can provide significant performance improvement; and when more complex learning algorithms are incorporated with evolution, the resulting algorithms are very promising and compete very well against the state of the art hybrid algorithms both in well-defined real-parameter function optimization problems and a practical evaluation-expensive problem

Optimal Inventory Control and Distribution Network Design of Multi-Echelon Supply Chains

Optimale Bestandskontrolle und Gestaltung von Vertriebsnetzen mehrstufiger Supply Chains Aufgrund von Global Sourcing, Outsourcing der Produktion und Versorgung weltweiter Kunden innerhalb eines komplexen Vertriebsnetzes, in welchem mehrere Anlagen durch verschiedene Aktivitäten miteinander vernetzt sind, haben die meisten Unternehmen heutzutage immer komplexere Supply Chain-Netzwerke in einer immer unbeständiger werdenden Geschäftsumgebung. Mehr beteiligte Unternehmen in der Wertschöpfungskette bedeuten mehr Knoten und Verbindungen im Netzwerk. Folglich bringt die Globalisierung Komplexität und neue Herausforderungen, obwohl Unternehmen immer stärker von globalen Supply Chains profitieren. In einer solchen Geschäftsumgebung müssen sich die Akteure innerhalb der Supply Chain (SC) auf die effiziente Verwaltung und Koordination des Materialflusses im mehrstufigen System fokussieren, um diesen Herausforderungen handhaben zu können. In vielen Fällen beinhaltet die Supply Chain eines Unternehmens unterschiedliche Entscheidungen auf verschiedenen Planungsebenen, wie der Anlagenstandort, die Bestände und die Verkehrsmittel. Jede dieser Entscheidungen spielt eine bedeutende Rolle hinsichtlich der Gesamtleistung und das Verhältnis zwischen ihnen kann nicht ignoriert werden. Allerdings wurden diese Entscheidungen meist einzeln untersucht. In den letzten Jahren haben zahlreiche Studien die Bedeutung der Integration von beteiligten Entscheidungen in Supply Chains hervorgehoben. In diesem Zusammenhang sollten Entscheidungen über Anlagenstandort, Bestand und Verkehrsmittel gemeinsam in einem Optimierungsproblem des Vertriebsnetzes betrachtet werden, um genauere Ergebnisse für das Gesamtsystem zu erzeugen. Darüber hinaus ist ein effektives Management des Materialflusses über die gesamte Lieferkette hinweg, aufgrund der dynamischen Umgebung mit mehreren Zielen, ein schwieriges Problem. Die Lösungsansätze, die in der Vergangenheit verwendet wurden, um Probleme mehrstufiger Supply Chains zu lösen, basierten auf herkömmlichen Verfahren unter der Verwendung von analytischen Techniken. Diese sind jedoch nicht ausreichend, um die Dynamiken in Lieferketten zu bewältigen, aufgrund ihrer Unfähigkeit, mit den komplexen Interaktionen zwischen den Akteuren der Supply Chain umzugehen und das stochastische Verhalten zu repräsentieren, das in vielen Problemen der realen Welt besteht. Die Simulationsmodellierung ist in letzter Zeit zu einem wichtigen Instrument geworden, da ein analytisches Modell nicht in der Lage ist, ein System abzubilden, das sowohl der Variabilität als auch der Komplexität unterliegt. Allerdings erfordern Simulationen umfangreiche Laufzeiten, um möglichst viele Lösungen zu bewerten und die optimale Lösung für ein definiertes Problem zu finden. Um mit dieser Schwierigkeit umzugehen, muss das Simulationsmodell in Optimierungsalgorithmen integriert werden. In Erwiderung auf die oben genannten Herausforderungen, ist eines der Hauptziele dieser Arbeit, ein Modell und ein Lösungsverfahren für die optimale Gestaltung von Vertriebsnetzwerken integrierter Supply Chains vorzuschlagen, das die Beziehung zwischen den Entscheidungen der verschiedenen Planungsebenen berücksichtigt. Die Problemstellung wird mithilfe von Zielfunktionen formuliert, um die Kundenabdeckung zu maximieren, den maximalen Abstand von den Anlagenstandorten zu den Bedarfspunkten zu minimieren oder die Gesamtkosten zu minimieren. Um die optimale Anzahl, Kapazität und Lage der Anlagen zu bestimmen, kommen der Nondominated Sorting Genetic Algorithm II (NSGA-II) und der Quantum-based Particle Swarm Optimization Algorithm (QPSO) zum Einsatz, um dieses Optimierungsproblem im Spannungsfeld verschiedener Ziele zu lösen. Aufgrund der Komplexität mehrstufiger Systeme und der zugrunde liegenden Unsicherheiten, wurde die Optimierung von Beständen über die gesamte Lieferkette hinweg zur wesentlichen Herausforderung, um die Kosten zu reduzieren und die Serviceanforderungen zu erfüllen. In diesem Zusammenhang ist das andere Ziel dieser Arbeit die Darstellung eines simulationsbasierten Optimierungs-Frameworks, in dem die Simulation, basierend auf der objektorientierten Programmierung, entwickelt wird und die Optimierung metaheuristische Techniken mit unterschiedlichen Kriterien, wie NSGA-II und MOPOSO, verwendet. Insbesondere das geplante Framework regt einen großen Nutzen an, sowohl für das Bestandsoptimierungsproblem in mehrstufigen Supply Chains, als auch für andere Logistikprobleme.Today, most companies have more complex supply chain networks in a more volatile business environment due to global sourcing, outsourcing of production and serving customers all over the world with a complex distribution network that has several facilities linked by various activities. More companies involved within the value chain, means more nodes and links in the network. Therefore, globalization brings complexities and new challenges as enterprises increasingly benefit from global supply chains. In such a business environment, Supply Chain (SC) members must focus on the efficient management and coordination of material flow in the multi-echelon system to handle with these challenges. In many cases, the supply chain of a company includes various decisions at different planning levels, such as facility location, inventory and transportation. Each of these decisions plays a significant role in the overall performance and the relationship between them cannot be ignored. However, these decisions have been mostly studied individually. In recent years, numerous studies have emphasized the importance of integrating the decisions involved in supply chains. In this context, facility location, inventory and transportation decisions should be jointly considered in an optimization problem of distribution network design to produce more accurate results for the whole system. Furthermore, effective management of material flow across a supply chain is a difficult problem due to the dynamic environment with multiple objectives. In the past, the majority of the solution approaches used to solve multi-echelon supply chain problems were based on conventional methods using analytical techniques. However, they are insufficient to cope with the SC dynamics because of the inability to handle to the complex interactions between the SC members and to represent stochastic behaviors existing in many real world problems. Simulation modeling has recently become a major tool since an analytical model is unable to formulate a system that is subject to both variability and complexity. However, simulations require extensive runtime to evaluate many feasible solutions and to find the optimal one for a defined problem. To deal with this problem, simulation model needs to be integrated in optimization algorithms. In response to the aforementioned challenges, one of the primary objectives of this thesis is to propose a model and solution method for the optimal distribution network design of an integrated supply chain that takes into account the relationship between decisions at the different levels of planning horizon. The problem is formulated with objective functions to maximize the customer coverage or minimize the maximal distance from the facilities to the demand points and minimize the total cost. In order to find optimal number, capacity and location of facilities, the Nondominated Sorting Genetic Algorithm II (NSGA-II) and Quantum-based Particle Swarm Optimization Algorithm (QPSO) are employed for solving this multiobjective optimization problem. Due to the complexities of multi-echelon system and the underlying uncertainty, optimizing inventories across the supply chain has become other major challenge to reduce the cost and to meet service requirements. In this context, the other aim of this thesis is to present a simulation-based optimization framework, in which the simulation is developed based on the object-oriented programming and the optimization utilizes multi-objective metaheuristic techniques, such as the well-known NSGA-II and MOPSO. In particular, the proposed framework suggests a great utility for the inventory optimization problem in multi-echelon supply chains, as well as for other logistics-related problems

Equiconvergence in summation associated with elliptic polinomial

We compare the Fourier integral with the Fourier series in summation associated with elliptic polinomial

Evolutionary Agent-Based Policy Analysis in Dynamic Environments

Eiben, A.E. [Promotor]Bergh, J.C.J.M. van den [Promotor