Knowledge acquisition issues for intelligent route optimization by evolutionary computation. - In: IEEE Congress on Evolutionary Computation (CEC), 2014, ISBN 978-1-4799-1486-9, (2014), S. 3252-3257
The paper introduces a Knowledge Acquisition and Maintenance concept for a Case Based Approximation method to solve large scale Traveling Salesman Problems in a short time (around 3 seconds) with an error rate below 3 %. This method is based on the insight, that most solutions are very similar to solutions that have been created before. Thus, in many cases a solution can be derived from former solutions by (1) selecting a most similar TSP from a library of former TSP solutions, (2) removing the locations that are not part of the current TSP and (3) adding the missing locations of the current TSP by mutation, namely Nearest Insertion (NI). This way of creating solutions by Case Based Reasoning (CBR) avoids the computational costs to create new solutions from scratch.
http://dx.doi.org/10.1109/CEC.2014.6900415
A case based approach for an intelligent route optimization technology. - In: Proceedings and companion publication of the 2014 Genetic and Evolutionary Computation Conference, July 12 - 16, 2014, Vancouver, BC, Canada ; a recombination of the 23rd International Conference on Genetic Algorithms (ICGA) and the 19th Annual Genetic Programming Conference (GP) ; one conference - many mini-conferences ; [and co-located workshops proceedings], ISBN 978-1-4503-2663-6, (2014), S. 1069-1072
The paper introduces a Case Based Approximation method to solve large scale Traveling Salesman Problems in a short time with a low error rate. It is useful for domains with most solutions being similar to solutions that have been created. Thus, a solution can be derived by (1) selecting a most similar TSP from a library of former TSP solutions, (2) removing the locations that are not part of the current TSP and (3) adding the missing locations of the current TSP by mutation, namely Nearest Insertion (NI). This way of creating solutions by Case Based Reasoning (CBR) avoids the computational costs to create new solutions from scratch.
Optimizing university curricula through correlation analysis. - In: International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), 2013, ISBN 978-1-4799-3212-2, (2013), S. 324-329
In this paper, we introduce a refined Educational Data Mining approach, which refrains from explicit learner modeling along with an evaluation concept. We use a Data Mining technology, which models students' learning characteristics by considering real data instead of deriving their characteristics explicitly. It aims at mining course characteristics similarities of former students' study traces and utilizing them to optimize curricula of current students based to their performance traits revealed by their educational history. This refined technology generates suggestions of personalized curricula. The technology includes an adaptation mechanism, which compares recent data with historical data to ensure that the similarity of mined characteristics follow the dynamic changes affecting curriculum (e.g., revision of course contents and materials, and changes in teachers, etc.). Finally, the paper shows some pre-validation results and approaches for a final validation.
http://dx.doi.org/10.1109/SITIS.2013.60
An adaptive system for optimal matches between human needs and offers. - In: International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), 2013, ISBN 978-1-4799-3212-2, (2013), S. 317-323
The paper presents a very general and many purpose technique to represent human needs and offers along with a technology to find optimal matches. Moreover, the system is able to learn from its use by collecting user feedback and changing its parameters accordingly. This way, the system adjusts itself to the human expectations and desires and even follows the trend of these desires and expectations.
http://dx.doi.org/10.1109/SITIS.2013.59
An approach to consider diversity issues from a semantic point of view. - In: IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2013, ISBN 978-1-4799-0650-5, (2013), S. 1696-1701
In this paper, we discuss a semantic and application-driven approach to estimate diversity respectively similarity in Genetic Algorithms (GA) based on a relative distance. This diversity metric can used to decide, whether or not a new individual meets a requested degree of diversity. Furthermore, the trade-off between several versions of the metric and their computational complexity is discussed. Finally, the application of this metric and a formerly developed Backtrack- and Restart GA to solve the Travelling Salesman Problem under certain real time requirements is introduced along with experimental evaluation.
https://doi.org/10.1109/SMC.2013.292
Context-assisted test cases reduction for cloud validation. - In: Modeling and Using Context, (2013), S. 288-301
Cloud computing is currently receiving much attention from the industry, government, and academia. It has changed the way computation is performed and how services are delivered to customers. Most importantly, cloud services change the way software is designed, how data is handled, and how testing is performed. In cloud computing, testing is delivered as a service (TaaS). For instance, case testing (one of the most common validation approaches) could be used. However, executing test cases on a cloud system could be expensive and time consuming. Therefore, test case reduction is performed to minimize the number of test cases to be executed on the system. In this paper, we introduce a validation method called Context-Assisted Test Case Reduction (CATCR) for systems that are deployed on the cloud. In CATCR, test cases are reduced based on the context of the validation process. The results of previous test cases are used to select test cases for the next iteration. The minimized set of test cases needs to have effective coverage of the system on the cloud. To evaluate CATCR, an experimental evaluation is performed through Amazon's Cloud and a Java validation tool. Experimental results are recorded and presented.
http://dx.doi.org/10.1007/978-3-642-40972-1_22
Adaptive kansei search method using user's subjective criterion deviation. - In: Intelligent computer vision and image processing, ISBN 978-1-4666-3906-5, (2013), S. 14-26
Structural diversity for genetic algorithms and its use for creating individuals. - In: IEEE Congress on Evolutionary Computation (CEC), 2013, ISBN 978-1-4799-0453-2, (2013), S. 783-788
The paper presents a structural representation of diversity (respectively similarity). This representation can be used to decide, whether or not a new individual meets a requested degree of diversity, but also to estimate and optimize a populations coverage of the solution space to avoid running into a local optimum and missing the global one. Moreover, it can also be constructively used for systematically creating new individuals, which (1) meet a certain diversity requirement, (2) additionally improve the coverage of the solution search space, and (3) have an optimal fitness value.
http://dx.doi.org/10.1109/CEC.2013.6557648
Curriculum optimization by correlation analysis and its validation. - In: Human-Computer Interaction and Knowledge Discovery in Complex, Unstructured, Big Data, (2013), S. 311-318
The paper introduces a refined Educational Data Mining approach, which refrains from explicit learner modeling along with an evaluation concept. The technology is a "lazy" Data Mining technology, which models students' learning characteristics by considering real data instead of deriving ("guessing") their characteristics explicitly. It aims at mining course characteristics similarities of former students' study traces and utilizing them to optimize curricula of current students based to their performance traits revealed by their educational history. This (compared to a former publication) refined technology generates suggestions of personalized curricula. The technology is supplemented by an adaptation mechanism, which compares recent data with historical data to ensure that the similarity of mined characteristics follow the dynamic changes affecting curriculum (e.g., revision of course contents and materials, and changes in teachers, etc.). Finally, the paper derives some refinement ideas for the evaluation method.
http://dx.doi.org/10.1007/978-3-642-39146-0_28
An intelligent system for modeling and supporting academic educational processes. - In: Intelligent and adaptive educational-learning systems, (2013), S. 469-496
University has a complicated system of course offerings, registration rules, and prerequisite courses, which should be matched to students' dynamic learning needs, and desires. We address this problem by developing an Educational-Learning System called "Dynamic Storyboarding System". Besides modeling learning processes, this system aims at evaluating and refining university curricula to reach an optimum of learning success in terms of best possible ac-cumulative grade point average (GPA). This is performed by applying Educational Data Mining (EDM) to former students curricula and their degree of success (GPA) and thus, uncovering golden didactic knowledge for successful education. It consists of mining a decision tree (DT) and applying it to curricula planned by current students. Students receive an estimation of the GPA they are likely to receive along with a recommendation to supplement a partial path to reach optimal success. Our approach includes individual learner profiles. The profiling concept initially uses the per-university educational history and is dynamically extended by the students' university study results. The profiles are used by applying the EDM technology to students with profiles of a high similarity to the student under consideration. A feasibility study showed the usefulness of the system. The effect has been validated by cross-validation with about 200 students' records. The mean of the difference between the original grade point average (GPA) and the estimated one was 0.43 with a standard deviation of 0.30.