Predicting expected TCP throughput using genetic algorithm

Predicting the expected throughput of TCP is important for several aspects such as e.g. determining handover criteria for future multihomed mobile nodes or determining the expected throughput of a given MPTCP subflow for load-balancing reasons. However, this is challenging due to time varying behavior of the underlying network characteristics. In this paper, we present a genetic-algorithm-based prediction model for estimating TCP throughput values. Our approach tries to find the best matching combination of mathematical functions that approximate a given time series that accounts for the TCP throughput samples using genetic algorithm. Based on collected historical datapoints about measured TCP throughput samples, our algorithm estimates expected throughput over time. We evaluate the quality of the prediction using different selection and diversity strategies for creating new chromosomes. Also, we explore the use of different fitness functions in order to evaluate the goodness of a chromosome. The goal is to show how different tuning on the genetic algorithm may have an impact on the prediction. Using extensive simulations over several TCP throughput traces, we find that the genetic algorithm successfully finds reasonable matching mathematical functions that allow to describe the TCP sampled throughput values with good fidelity. We also explore the effectiveness of predicting time series throughput samples for a given prediction horizon and estimate the prediction error and confidence.Peer ReviewedPostprint (author's final draft

Machine Learning Algorithms in Cloud Manufacturing - A Review

Cloud computing has advanced significantly in terms of storage, QoS, online service availability, and integration with conventional business models and procedures. The traditional manufacturing firm becomes Cloud Manufacturing when Cloud Services are integrated into the present production process. The capabilities of Cloud Manufacturing are enhanced by Machine Learning. A lot of machine learning algorithms provide the user with the desired outcomes. The main objectives are to learn more about the architecture and analysis of Cloud Manufacturing frameworks and the role that machine learning algorithms play in cloud computing in general and Cloud Manufacturing specifically. Machine learning techniques like SVM, Genetic Algorithm, Ant Colony Optimisation techniques, and variants are employed in a cloud environment

A modified genetic algorithm with a new crossover mating scheme

This study introduced the Inversed Bi-segmented Average Crossover (IBAX), a novel crossover operator that enhanced the offspring generation of the genetic algorithm (GA) for variable minimization and numerical optimization problems. An attempt to come up with a new mating scheme in generating new offspring under the crossover function through the novel IBAX operator has paved the way to a more efficient and optimized solution for variable minimization particularly on premature convergence problem using GA. A total of 597 records of student-respondents in the evaluation of the faculty instructional performance, represented by 30 variables, from the four State Universities and Colleges (SUC) in Caraga Region, Philippines were used as the dataset.  The simulation results showed that the proposed modification on the Average Crossover (AX) of the genetic algorithm outperformed the genetic algorithm with the original AX operator. The GA with IBAX operator combined with rank-based selection function has removed 20 or 66.66% of the variables while 13 or 43.33% of the variables were removed when GA with AX operator and roulette wheel selection function was used

A New Adaptive Hungarian Mating Scheme in Genetic Algorithms

In genetic algorithms, selection or mating scheme is one of the important operations. In this paper, we suggest an adaptive mating scheme using previously suggested Hungarian mating schemes. Hungarian mating schemes consist of maximizing the sum of mating distances, minimizing the sum, and random matching. We propose an algorithm to elect one of these Hungarian mating schemes. Every mated pair of solutions has to vote for the next generation mating scheme. The distance between parents and the distance between parent and offspring are considered when they vote. Well-known combinatorial optimization problems, the traveling salesperson problem, and the graph bisection problem are used for the test bed of our method. Our adaptive strategy showed better results than not only pure and previous hybrid schemes but also existing distance-based mating schemes

유전 알고리즘에서의 적응적 짝짓기 제도

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2017. 2. 문병로.짝짓기 제도는 자식 해를 만들기 위하여 두 부모를 선택하는 방법을 말한다. 이는 유전 알고리즘의 동작 전반에 영향을 끼친다. 본 논문에서 는,헝가리안방법을사용한짝짓기제도에대해연구하였다.그제도들은 대응되는 거리의 합을 최소화하는 방법, 최대화하는 방법, 그리고 비교를 위해 랜덤하게 대응시키는 방법들을 가리킨다. 본 논문에서는 이 제도들 을잘알려진문제인순회판매원문제와그래프분할문제에적용하였다. 또한 세대별로 가장 좋은 해가 어떻게 변화하는지 분석하였다. 이러한 분 석에 기초하여, 본 논문에서는 간단히 결합된 짝짓기 제도를 제안하였다. 제안된 제도는 결합되지 않은 제도에 비해 더 좋은 결과를 보였다. 본 논문에서는 또한, 본 논문의 핵심 방법인 짝짓기 제도를 결합하는 방법을 제안한다. 본 논문의 적응적인 짝짓기 방법은 세 헝가리안 제도 중하나를선택한다.모든짝지어진쌍은다음세대를위한짝짓기방법을 결정할 투표권을 갖게 된다. 각각의 선호도는 부모해간 거리와 부모해와 자식해의 거리의 비율을 통해 결정된다. 제안된 적응적 방법은 모든 단일 헝가리안짝짓기제도,비적응적으로결합된방법,전통적인룰렛휠선택, 기존의다른거리기준방법들보다좋은결과를보였다.제안된적응적방 법은정기적인해집단의유입과지역최적화와결합된환경에서도적절한 제도를 선택했다. 본 논문에서는 헝가리안 방법을 최대 혹은 최소의 지역 최적점을찾는방법으로교체했다.이방식역시지역최적점을찾는단일 방법들보다 좋은 결과를 보였다I. Introduction 1 1.1 Motivation 1 1.2 Related Work 2 1.3 Contribution 4 1.4 Organization 6 II. Preliminary 7 2.1 Hungarian Method 7 2.2 Geometric Operators 10 2.2.1 Formal Definitions 10 2.3 Exploration Versus Exploitation Trade-off 11 2.4 Test Problems and Distance Metric 13 III. Hungarian Mating Scheme 15 3.1 Proposed Scheme 15 3.2 Tested GA 18 3.3 Observation 18 3.3.1 Traveling Salesman Problem 18 3.3.2 Graph Bisection Problem 21 IV. Hybrid and Adaptive Scheme 28 4.1 Simple Hybrid Scheme 28 4.2 Adaptive Scheme 30 4.2.1 Significance of Adaptive Scheme 30 4.2.2 Proposed Method 31 4.2.3 Theoretical Support 34 4.2.4 Experiments 36 4.2.5 Traveling Salesman Problem 36 4.2.6 Graph Bisection Problem 40 4.2.7 Comparison with Traditional Method 41 4.2.8 Comparison with Distance-based Methods 42 V. Tests in Various Environments 50 5.1 Hybrid GA 50 5.1.1 Experiment Settings 50 5.1.2 Results and Discussions 51 5.2 GA with New Individuals 52 5.2.1 Experiment Settings 52 5.2.2 Results and Discussions 53 VI. A Revised Version of Adaptive Method 62 6.1 Hungarian Mating Scheme 62 6.2 Experiment Settings 62 6.3 Results and Discussions 63 VII. Conclusion 67 7.1 Summary 67 7.2 Future Work 68Docto

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