Traveling Salesman Problem

The idea behind TSP was conceived by Austrian mathematician Karl Menger in mid 1930s who invited the research community to consider a problem from the everyday life from a mathematical point of view. A traveling salesman has to visit exactly once each one of a list of m cities and then return to the home city. He knows the cost of traveling from any city i to any other city j. Thus, which is the tour of least possible cost the salesman can take? In this book the problem of finding algorithmic technique leading to good/optimal solutions for TSP (or for some other strictly related problems) is considered. TSP is a very attractive problem for the research community because it arises as a natural subproblem in many applications concerning the every day life. Indeed, each application, in which an optimal ordering of a number of items has to be chosen in a way that the total cost of a solution is determined by adding up the costs arising from two successively items, can be modelled as a TSP instance. Thus, studying TSP can never be considered as an abstract research with no real importance

Analysis and Visualization of Local Phylogenetic Structure within Species

While it is interesting to examine the evolutionary history and phylogenetic relationship between species, for example, in a sort of tree of life, there is also a great deal to be learned from examining population structure and relationships within species. A careful description of phylogenetic relationships within species provides insights into causes of phenotypic variation, including disease susceptibility. The better we are able to understand the patterns of genotypic variation within species, the better these populations may be used as models to identify causative variants and possible therapies, for example through targeted genome-wide association studies (GWAS). My thesis describes a model of local phylogenetic structure, how it can be effectively derived under various circumstances, and useful applications and visualizations of this model to aid genetic studies. I introduce a method for discovering phylogenetic structure among individuals of a population by partitioning the genome into a minimal set of intervals within which there is no evidence of recombination. I describe two extensions of this basic method. The first allows it to be applied to heterozygous, in addition to homozygous, genotypes and the second makes it more robust to errors in the source genotypes. I demonstrate the predictive power of my local phylogeny model using a novel method for genome-wide genotype imputation. This imputation method achieves very high accuracy - on the order of the accuracy rate in the sequencing technology - by imputing genotypes in regions of shared inheritance based on my local phylogenies. Comparative genomic analysis within species can be greatly aided by appropriate visualization and analysis tools. I developed a framework for web-based visualization and analysis of multiple individuals within a species, with my model of local phylogeny providing the underlying structure. I will describe the utility of these tools and the applications for which they have found widespread use.Doctor of Philosoph

Improving resiliency using graph based evolutionary algorithms

Resiliency is an important characteristic of any system. It signifies the ability of a system to survive and recover from unprecedented disruptions. Various characteristics exist that indicate the level of resiliency in a system. One of these attributes is the adaptability of the system. This adaptability can be enhanced by redundancy present within the system. In the context of system design, redundancy can be achieved by having a diverse set of good designs for that particular system. Evolutionary algorithms are widely used in creating designs for engineering systems, as they perform well on discontinuous and/or high dimensional problems. One method to control the diversity of solutions within an evolutionary algorithm is the use of combinatorial graphs, or graph based evolutionary algorithms. This diversity of solutions is key factor to enhance the redundancy of a system design. In this work, the way how graph based evolutionary algorithms generate diverse solutions is investigated by examining the influence of representation and mutation. This allows for greater understanding of the exploratory nature of each representation and how they can control the number of solution generated within a trial. The results of this research are then applied to the Travelling [sic] Salesman Problem, a known NP hard problem often used as a surrogate for logistic or network design problems. When the redundancy in system design is improved, adaptability can be achieved by placing an agent to initiate a transfer to other good solutions in the event of a disruption in network connectivity, making it possible to improve the resiliency of the system --Abstract, page iii

Improving Quality of the Solution for the Team Formation Problem in Social Networks Using SCAN Variant and Evolutionary Computation

Social Network Analysis helps to visualize and understand the roles and relationships that ease or impede the collaboration and sharing of the information and knowledge in an organization. In this research work, we will focus on the Team Formation Problem (TFP) which is an open problem where we need to identify an ideal team, with members of complementary talent or skills, to solve any given task. Current research suggests that TFP solutions have been attempted with evolutionary computation approach using Cultural Algorithms (CA) and Genetic Algorithms (GA). However, SCAN (Structural Clustering Algorithm for Networks) variants such as WSCAN (Weighted Structural Clustering Algorithm for Networks) demonstrate a high capability to find solutions for another type of network problems. In this thesis, we first propose to use WSCAN-TFP algorithm to deal with the problem of team formation in social networks, and we our findings indicate that WSCAN-TFP algorithm worked faster than the evolutionary algorithms counterparts but was of lower performance compared to CAs and GAs. Next, we propose two hybrid solutions by combining GA and CA with a modified WSCAN-TFP algorithm. To test the performance of our proposed approaches, we define multiple quality criteria based on communication cost (CC), average fitness score (AFS) and average processing time. We used big datasets from DBLP nodes network with sizes 50K and 100K. The results show that our proposed methods HGA and HCA can find the near-optimal solutions faster with minimum communication cost with the improvement of $\approx 66\%$ and $\approx 57\%$ in average fitness in comparison to existing GA and CA methods respectively

Training Neural Networks Through the Integration of Evolution and Gradient Descent

Neural networks have achieved widespread adoption due to both their applicability to a wide range of problems and their success relative to other machine learning algorithms. The training of neural networks is achieved through any of several paradigms, most prominently gradient-based approaches (including deep learning), but also through up-and-coming approaches like neuroevolution. However, while both of these neural network training paradigms have seen major improvements over the past decade, little work has been invested in developing algorithms that incorporate the advances from both deep learning and neuroevolution. This dissertation introduces two new algorithms that are steps towards the integration of gradient descent and neuroevolution for training neural networks. The first is (1) the Limited Evaluation Evolutionary Algorithm (LEEA), which implements a novel form of evolution where individuals are partially evaluated, allowing rapid learning and enabling the evolutionary algorithm to behave more like gradient descent. This conception provides a critical stepping stone to future algorithms that more tightly couple evolutionary and gradient descent components. The second major algorithm (2) is Divergent Discriminative Feature Accumulation (DDFA), which combines a neuroevolution phase, where features are collected in an unsupervised manner, with a gradient descent phase for fine tuning of the neural network weights. The neuroevolution phase of DDFA utilizes an indirect encoding and novelty search, which are sophisticated neuroevolution components rarely incorporated into gradient descent-based systems. Further contributions of this work that build on DDFA include (3) an empirical analysis to identify an effective distance function for novelty search in high dimensions and (4) the extension of DDFA for the purpose of discovering convolutional features. The results of these DDFA experiments together show that DDFA discovers features that are effective as a starting point for gradient descent, with significant improvement over gradient descent alone. Additionally, the method of collecting features in an unsupervised manner allows DDFA to be applied to domains with abundant unlabeled data and relatively sparse labeled data. This ability is highlighted in the STL-10 domain, where DDFA is shown to make effective use of unlabeled data

A Genetic Algorithm for the Vehicle Routing Problem

The purpose of this research was to develop a version of a genetic algorithm (GA ) which would provide near optimal solutions for Vehicle Routing Problems (VRP) with both time and weight constraints. The genetic algorithm used for the experimentation was adapted from a GA which had been developed by James Bean at the University of Michigan to solve machine scheduling problems. The VRP data sets used in this research were obtained from the literature. Various aspects of the GA were experimented with in order to develop a version which would perform consistently well for all the data sets. The results of the final version of the genetic algorithm were then compared to the results presented in the original papers. The results from this research indicated that the genetic algorithm seems to perform relatively well for smaller problems with 50 or fewer customers. However, the results seem to become progressively worse as the problem becomes larger

Model based test suite minimization using metaheuristics

Software testing is one of the most widely used methods for quality assurance and fault detection purposes. However, it is one of the most expensive, tedious and time consuming activities in software development life cycle. Code-based and specification-based testing has been going on for almost four decades. Model-based testing (MBT) is a relatively new approach to software testing where the software models as opposed to other artifacts (i.e. source code) are used as primary source of test cases. Models are simplified representation of a software system and are cheaper to execute than the original or deployed system. The main objective of the research presented in this thesis is the development of a framework for improving the efficiency and effectiveness of test suites generated from UML models. It focuses on three activities: transformation of Activity Diagram (AD) model into Colored Petri Net (CPN) model, generation and evaluation of AD based test suite and optimization of AD based test suite. Unified Modeling Language (UML) is a de facto standard for software system analysis and design. UML models can be categorized into structural and behavioral models. AD is a behavioral type of UML model and since major revision in UML version 2.x it has a new Petri Nets like semantics. It has wide application scope including embedded, workflow and web-service systems. For this reason this thesis concentrates on AD models. Informal semantics of UML generally and AD specially is a major challenge in the development of UML based verification and validation tools. One solution to this challenge is transforming a UML model into an executable formal model. In the thesis, a three step transformation methodology is proposed for resolving ambiguities in an AD model and then transforming it into a CPN representation which is a well known formal language with extensive tool support. Test case generation is one of the most critical and labor intensive activities in testing processes. The flow oriented semantic of AD suits modeling both sequential and concurrent systems. The thesis presented a novel technique to generate test cases from AD using a stochastic algorithm. In order to determine if the generated test suite is adequate, two test suite adequacy analysis techniques based on structural coverage and mutation have been proposed. In terms of structural coverage, two separate coverage criteria are also proposed to evaluate the adequacy of the test suite from both perspectives, sequential and concurrent. Mutation analysis is a fault-based technique to determine if the test suite is adequate for detecting particular types of faults. Four categories of mutation operators are defined to seed specific faults into the mutant model. Another focus of thesis is to improve the test suite efficiency without compromising its effectiveness. One way of achieving this is identifying and removing the redundant test cases. It has been shown that the test suite minimization by removing redundant test cases is a combinatorial optimization problem. An evolutionary computation based test suite minimization technique is developed to address the test suite minimization problem and its performance is empirically compared with other well known heuristic algorithms. Additionally, statistical analysis is performed to characterize the fitness landscape of test suite minimization problems. The proposed test suite minimization solution is extended to include multi-objective minimization. As the redundancy is contextual, different criteria and their combination can significantly change the solution test suite. Therefore, the last part of the thesis describes an investigation into multi-objective test suite minimization and optimization algorithms. The proposed framework is demonstrated and evaluated using prototype tools and case study models. Empirical results have shown that the techniques developed within the framework are effective in model based test suite generation and optimizatio

Enhanced Deep Network Designs Using Mitochondrial DNA Based Genetic Algorithm And Importance Sampling

Machine learning (ML) is playing an increasingly important role in our lives. It has already made huge impact in areas such as cancer diagnosis, precision medicine, self-driving cars, natural disasters predictions, speech recognition, etc. The painstakingly handcrafted feature extractors used in the traditional learning, classification and pattern recognition systems are not scalable for large-sized datasets or adaptable to different classes of problems or domains. Machine learning resurgence in the form of Deep Learning (DL) in the last decade after multiple AI (artificial intelligence) winters and hype cycles is a result of the convergence of advancements in training algorithms, availability of massive data (big data) and innovation in compute resources (GPUs and cloud). If we want to solve more complex problems with machine learning, we need to optimize all three of these areas, i.e., algorithms, dataset and compute. Our dissertation research work presents the original application of nature-inspired idea of mitochondrial DNA (mtDNA) to improve deep learning network design. Additional fine-tuning is provided with Monte Carlo based method called importance sampling (IS). The primary performance indicators for machine learning are model accuracy, loss and training time. The goal of our dissertation is to provide a framework to address all these areas by optimizing network designs (in the form of hyperparameter optimization) and dataset using enhanced Genetic Algorithm (GA) and importance sampling. Algorithms are by far the most important aspect of machine learning. We demonstrate the application of mitochondrial DNA to complement the standard genetic algorithm for architecture optimization of deep Convolution Neural Network (CNN). We use importance sampling to reduce the dataset variance and sample more often from the instances that add greater value from the training outcome perspective. And finally, we leverage massive parallel and distributed processing of GPUs in the cloud to speed up training. Thus, our multi-approach method for enhancing deep learning combines architecture optimization, dataset optimization and the power of the cloud to drive better model accuracy and reduce training time

Advances in Evolutionary Algorithms

With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field