An ACO-based Hyper-heuristic for Sequencing Many-objective Evolutionary Algorithms that Consider Different Ways to Incorporate the DM's Preferences

Many-objective optimization is an area of interest common to researchers, professionals, and practitioners because of its real-world implications. Preference incorporation into Multi-Objective Evolutionary Algorithms (MOEAs) is one of the current approaches to treat Many-Objective Optimization Problems (MaOPs). Some recent studies have focused on the advantages of embedding preference models based on interval outranking into MOEAs; several models have been proposed to achieve it. Since there are many factors influencing the choice of the best outranking model, there is no clear notion of which is the best model to incorporate the preferences of the decision maker into a particular problem. This paper proposes a hyper-heuristic algorithm—named HyperACO—that searches for the best combination of several interval outranking models embedded into MOEAs to solve MaOPs. HyperACO is able not only to select the most appropriate model but also to combine the already existing models to solve a specific MaOP correctly. The results obtained on the DTLZ and WFG test suites corroborate that HyperACO can hybridize MOEAs with a combined preference model that is suitable to the problem being solved. Performance comparisons with other state-of-the-art MOEAs and tests for statistical significance validate this conclusion

Maximizing User Engagement In Short Marketing Campaigns Within An Online Living Lab: A Reinforcement Learning Perspective

ABSTRACT MAXIMIZING USER ENGAGEMENT IN SHORT MARKETING CAMPAIGNS WITHIN AN ONLINE LIVING LAB: A REINFORCEMENT LEARNING PERSPECTIVE by ANIEKAN MICHAEL INI-ABASI August 2021 Advisor: Dr. Ratna Babu Chinnam Major: Industrial & Systems Engineering Degree: Doctor of Philosophy User engagement has emerged as the engine driving online business growth. Many firms have pay incentives tied to engagement and growth metrics. These corporations are turning to recommender systems as the tool of choice in the business of maximizing engagement. LinkedIn reported a 40% higher email response with the introduction of a new recommender system. At Amazon 35% of sales originate from recommendations, while Netflix reports that ‘75% of what people watch is from some sort of recommendation,’ with an estimated business value of $1 billion per year. While the leading companies have been quite successful at harnessing the power of recommenders to boost user engagement across the digital ecosystem, small and medium businesses (SMB) are struggling with declining engagement across many channels as competition for user attention intensifies. The SMBs often lack the technical expertise and big data infrastructure necessary to operationalize recommender systems. The purpose of this study is to explore the methods of building a learning agent that can be used to personalize a persuasive request to maximize user engagement in a data-efficient setting. We frame the task as a sequential decision-making problem, modelled as MDP, and solved using a generalized reinforcement learning (RL) algorithm. We leverage an approach that eliminates or at least greatly reduces the need for massive amounts of training data, thus moving away from a purely data-driven approach. By incorporating domain knowledge from the literature on persuasion into the message composition, we are able to train the RL agent in a sample efficient and operant manner. In our methodology, the RL agent nominates a candidate from a catalog of persuasion principles to drive higher user response and engagement. To enable the effective use of RL in our specific setting, we first build a reduced state space representation by compressing the data using an exponential moving average scheme. A regularized DQN agent is deployed to learn an optimal policy, which is then applied in recommending one (or a combination) of six universal principles most likely to trigger responses from users during the next message cycle. In this study, email messaging is used as the vehicle to deliver persuasion principles to the user. At a time of declining click-through rates with marketing emails, business executives continue to show heightened interest in the email channel owing to higher-than-usual return on investment of$42 for every dollar spent when compared to other marketing channels such as social media. Coupled with the state space transformation, our novel regularized Deep Q-learning (DQN) agent was able to train and perform well based on a few observed users’ responses. First, we explored the average positive effect of using persuasion-based messages in a live email marketing campaign, without deploying a learning algorithm to recommend the influence principles. The selection of persuasion tactics was done heuristically, using only domain knowledge. Our results suggest that embedding certain principles of persuasion in campaign emails can significantly increase user engagement for an online business (and have a positive impact on revenues) without putting pressure on marketing or advertising budgets. During the study, the store had a customer retention rate of 76% and sales grew by a half-million dollars from the three field trials combined. The key assumption was that users are predisposed to respond to certain persuasion principles and learning the right principles to incorporate in the message header or body copy would lead to higher response and engagement. With the hypothesis validated, we set forth to build a DQN agent to recommend candidate actions from a catalog of persuasion principles most likely to drive higher engagement in the next messaging cycle. A simulation and a real live campaign are implemented to verify the proposed methodology. The results demonstrate the agent’s superior performance compared to a human expert and a control baseline by a significant margin (~ up to 300%). As the quest for effective methods and tools to maximize user engagement intensifies, our methodology could help to boost user engagement for struggling SMBs without prohibitive increase in costs, by enabling the targeting of messages (with the right persuasion principle) to the right user

Cost-effective model-based test case generation and prioritization for software product line

In Software Product Line (SPL), testing is used to manage core assets that comprised variability and commonality in effective ways due to large sizes of products that continue to be developed. SPL testing requires a technique that is capable to manage SPL core assets. Model-based Testing (MBT) is a promising technique that offers automation and reusability in test cases generation. However, there are difficulties to ensure testing in MBT can achieve good test cases generation results based on cost (size of test suite, total execution time) and effectiveness (coverage criteria, fault detection rate) measures. This is due to lack of trade-off between cost and effectiveness in test cases generated in MBT for SPL. This study aims to increase quality of test cases based on cost and effectiveness by using generation and prioritization approaches for MBT in SPL. This study focuses on three parts to enhance quality of test cases. First, test model development based on traceability link. In order to improve test cases quality, this study focused on implementation of hybrid-based and hyper-heuristic based techniques to generate test cases. This is followed by Test Cases Prioritization (TCP) technique that is based on dissimilarity-based technique with string distance. These test cases generation and prioritization approaches are evaluated by using two benchmarks - one test object and one real object. The results are compared with other prominent approaches. The mapping approach showed 10.27% and 32.39% f-measure improvement against existing approach on e-shop object, respectively. For test cases generation using hybrid-based approach, the proposed approach outperformed existing approaches with 11.66% coverage, 17.78% average execution time, and 45.98% average size of test suite on vending machine object. The hyper-heuristic based approach NSGA-II-LHH outperformed other proposed low-level heuristic approaches with 12.00% improvement on coverage, 46.66% average execution time and 42.54% average size of test suite. Furthermore, evaluation of TCP approaches showed fault detection improvement of 21.60%, 10.40% and 12.20% and total execution time improvement of 48.00%, 22.70% and 31.80% in comparison with three existing approaches. The results revealed that proposed model transformations, test cases generation and prioritization approaches significantly improve cost and effectiveness measure in MBT for SPL

Selection Constructive based Hyper-heuristic for Dynamic Scheduling

A função de escalonamento desempenha um papel importante nos sistemas de produção. Os sistemas de escalonamento têm como objetivo gerar um plano de escalonamento que permite gerir de uma forma eficiente um conjunto de tarefas que necessitam de ser executadas no mesmo período de tempo pelos mesmos recursos. Contudo, adaptação dinâmica e otimização é uma necessidade crítica em sistemas de escalonamento, uma vez que as organizações de produção têm uma natureza dinâmica. Nestas organizações ocorrem distúrbios nas condições requisitos de trabalho regularmente e de forma inesperada. Alguns exemplos destes distúrbios são: surgimento de uma nova tarefa, cancelamento de uma tarefa, alteração na data de entrega, entre outros. Estes eventos dinâmicos devem ser tidos em conta, uma vez que podem influenciar o plano criado, tornando-o ineficiente. Portanto, ambientes de produção necessitam de resposta imediata para estes eventos, usando um método de reescalonamento em tempo real, para minimizar o efeito destes eventos dinâmicos no sistema de produção. Deste modo, os sistemas de escalonamento devem de uma forma automática e inteligente, ser capazes de adaptar o plano de escalonamento que a organização está a seguir aos eventos inesperados em tempo real. Esta dissertação aborda o problema de incorporar novas tarefas num plano de escalonamento já existente. Deste modo, é proposta uma abordagem de otimização – Hiper-heurística baseada em Seleção Construtiva para Escalonamento Dinâmico- para lidar com eventos dinâmicos que podem ocorrer num ambiente de produção, a fim de manter o plano de escalonamento, o mais robusto possível. Esta abordagem é inspirada em computação evolutiva e hiper-heurísticas. Do estudo computacional realizado foi possível concluir que o uso da hiper-heurística de seleção construtiva pode ser vantajoso na resolução de problemas de otimização de adaptação dinâmica.Scheduling plays an important role in manufacturing systems. It produces a scheduling plan, in order to share resources to produce several different products in the same time period. However, dynamic adaptation and optimization is a critical need in real-world manufacturing scheduling systems, since contemporary manufacturing organizations have a dynamic nature, where disturbances on working conditions and requirements occur on a continuous basis. Disturbances often arise unexpectedly, and can be for example: urgent job arrival, job cancelation, due date change, delay in the arrival, among others. These dynamic events must be taken into account, since they may have a major impact on the scheduling plan, they can disorder the plan making it ineffective. Therefore, manufacturing environments require immediate response to these dynamic events, using a real-time rescheduling method, in order to minimize the effect of such unexpected events in the performance of the production’ system. As result, scheduling systems should have the ability of automatically and intelligently maintain real-time adaptation and optimization to efficiently update the scheduling plan to the unexpected events. This way, the organization keeps clients satisfied and achieves its objectives (costs minimized and profits maximized). This dissertation addresses the problem of incorporating new tasks in a scheduling plan already generated by the scheduling system. Therefore, it proposes an optimization approach - Selection Constructive based Hyper-heuristic for Dynamic Scheduling - to deal with dynamic events that can occur over time in a manufacturing environment, with the main goal of maintaining the current scheduling plan feasible and most robust as possible. The development of this dynamic adaptation approach is inspired on evolutionary computation and hyper-heuristics. The viability of the proposed approach is tested by performing a set of experiments and analysing the results achieved. From the obtained results it is possible to conclude that the use of a selection constructive hyper-heuristic could be advantageous on solving dynamic adaptation optimization problems

Layered evaluation of interactive adaptive systems : framework and formative methods

Peer reviewedPostprin

Recommending Structured Objects: Paths and Sets

Recommender systems have been widely adopted in industry to help people find the most appropriate items to purchase or consume from the increasingly large collection of available resources (e.g., books, songs and movies). Conventional recommendation techniques follow the approach of ``ranking all possible options and pick the top'', which can work effectively for single item recommendation but fall short when the item in question has internal structures. For example, a travel trajectory with a sequence of points-of-interest or a music playlist with a set of songs. Such structured objects pose critical challenges to recommender systems due to the intractability of ranking all possible candidates. This thesis study the problem of recommending structured objects, in particular, the recommendation of path (a sequence of unique elements) and set (a collection of distinct elements). We study the problem of recommending travel trajectories in a city, which is a typical instance of path recommendation. We propose methods that combine learning to rank and route planning techniques for efficient trajectory recommendation. Another contribution of this thesis is to develop the structured recommendation approach for path recommendation by substantially modifying the loss function, the learning and inference procedures of structured support vector machines. A novel application of path decoding techniques helps us achieve efficient learning and recommendation. Additionally, we investigate the problem of recommending a set of songs to form a playlist as an example of the set recommendation problem. We propose to jointly learn user representations by employing the multi-task learning paradigm, and a key result of equivalence between bipartite ranking and binary classification enables efficient learning of our set recommendation method. Extensive evaluations on real world datasets demonstrate the effectiveness of our proposed approaches for path and set recommendation