Multi Objective Optimization of classification rules using Cultural Algorithms

AbstractClassification rule mining is the most sought out by users since they represent highly comprehensible form of knowledge. The rules are evaluated based on objective and subjective metrics. The user must be able to specify the properties of the rules. The rules discovered must have some of these properties to render them useful. These properties may be conflicting. Hence discovery of rules with specific properties is a multi objective optimization problem. Cultural Algorithm (CA) which derives from social structures, and which incorporates evolutionary systems and agents, and uses five knowledge sources (KS's) for the evolution process better suits the need for solving multi objective optimization problem. In the current study a cultural algorithm for classification rule mining is proposed for multi objective optimization of rules

Improving Robustness in Social Fabric-based Cultural Algorithms

In this thesis, we propose two new approaches which aim at improving robustness in social fabric-based cultural algorithms. Robustness is one of the most significant issues when designing evolutionary algorithms. These algorithms should be capable of adapting themselves to various search landscapes. In the first proposed approach, we utilize the dynamics of social interactions in solving complex and multi-modal problems. In the literature of Cultural Algorithms, Social fabric has been suggested as a new method to use social phenomena to improve the search process of CAs. In this research, we introduce the Irregular Neighborhood Restructuring as a new adaptive method to allow individuals to rearrange their neighborhoods to avoid local optima or stagnation during the search process. In the second approach, we apply the concept of Confidence Interval from Inferential Statistics to improve the performance of knowledge sources in the Belief Space. This approach aims at improving the robustness and accuracy of the normative knowledge source. It is supposed to be more stable against sudden changes in the values of incoming solutions. The IEEE-CEC2015 benchmark optimization functions are used to evaluate our proposed methods against standard versions of CA and Social Fabric. IEEE-CEC2015 is a set of 15 multi-modal and hybrid functions which are used as a standard benchmark to evaluate optimization algorithms. We observed that both of the proposed approaches produce promising results on the majority of benchmark functions. Finally, we state that our proposed strategies enhance the robustness of the social fabric-based CAs against challenges such as multi-modality, copious local optima, and diverse landscapes

Ubiquitous Learning Laboratory For Pediatric Nursing: A Cultural Algorithm Approach

ABSTRACT UBIQUITOUS LEARNING LABORATORY FOR PEDIATRIC NURSING: A CULTURAL ALGORITHM APPROACH by DAVID L. COLON December 2012 Advisor: Dr. Robert G. Reynolds Major: Computer Science Degree: Master of Science Quality Medical Care is at the focus of all health care service providers. Each facility maintains a standard level of care that promises not only a precise diagnosis, but also the correct course of treatment. In part, this is due to the educational training and professional experience of Nurses. For high-risk patients such as children, the level of expertise of a Pediatric Nurse is even more critical in order to guarantee patient safety. Pediatric Nurses do not necessarily have the same level of expertise in critical thinking and overall patient care, however. This can be attributed to variables in teaching institutions, training environments, and even demographic backgrounds. Moreover, the lack of a teaching paradigm that captures the attention of today\u27s technology-savvy student could also be a contributing factor. In this thesis, a learning framework is proposed that serves as an extension to accepted nursing curriculum. This framework is a 2d serious Educational Puzzle game based on the classic board game Clue. Clue involves solving a murder mystery utilizing character interaction and discovery / observation of objects within a given room. I-CARE is similar except this game involves determining a Medical Diagnoses utilizing patient interaction (i.e. character dialogue) and accessing various rooms (i.e. Class Room, Equipment, Patient, Medical Supplies, etc.) in order to deliver medical care. The I-CARE application encapsulates a virtual world that makes use of all perceptual modes (i.e. visual, auditory, and haptic), just like a real Children\u27s Hospital. The framework is developed for a mobile platform using XNA 4.0 technology. It offers a portable world where nurses can develop critical thinking skills and practice delivering quality medical care. With game play, they accumulate a progression of tasks required to deliver an Albuterol treatment to a pediatrics patient. These may not be the most efficient progression of tasks, however. Cultural Algorithms is an agent-based evolutionary method used to computationally determine the most efficient progression of tasks to deliver an Albuterol treatment. It begins by capturing all of the tasks available in the I-CARE virtual world. Next, it describes the rules of how those tasks can come together in terms of pre- and post-conditions of task usage. Finally, these rules are weighted in a manner that allows for task inclusion along with its relative position within the task progression. Cultural Algorithms is shown to be more than an experimental framework. It is also shown to be a learning mechanism as well. Through the execution of 10 runs at 1000 generations each, an analysis of the best learning example is performed. This analysis breaks down the progression of fitness scores over each generation to identify segments of learning. The idea is to not only determine an optimal solution to the stated problem, but to also identify how pediatric nurses learn themselves

The Use Of Cultural Algorithms To Learn The Impact Of Climate On Local Fishing Behavior In Cerro Azul, Peru

Recently it has been found that the earth’s oceans are warming at a pace that is 40% faster than predicted by a United Nations panel a few years ago. As a result, 2019 has become the warmest year on record for the earth’s oceans. That is because the oceans have acted as a buffer by absorbing 93% of the heat produced by the greenhouse gases [40]. The impact of the oceanic warming has already been felt in terms of the periodic warming of the Pacific Ocean as an effect of the ENSO process. The ENSO process is a cycle of warming and subsequent cooling of the Pacific Ocean that can last over a period of years. This cycle was first documented by Peruvian fishermen in the early 1600’s. So it has been part of the environmental challenges that have been presented to economic agents throughout the world since then. It has even been suggested that the cycle has increased in frequency over the years, perhaps in response to the overall issues related to global warming. Although the onset of the ENSO cycle might be viewed as disruption of the fishing economy in a given area, there is some possibility that over time agents have been able to develop strategic responses to these changes to as to reduce the economic risk associated with them. During that time the Cerro Azul, Peru was in the process of emerging from one of the largest ENSOs on record. This was perceived to be a great opportunity to see how the collective bodies of fishermen were able to alter their fishing strategies to deal with these more uncertain times. Our results suggest that indeed the collective economic response of the fishermen demonstrates an ability to respond to the unpredictabilities of climate change, but at a cost. It is clear that the fishermen have gained the collective knowledge over the years to produce a coordinated response that can be observed at a higher level. Of course, this knowledge can be used to coordinate activities only if it is communicated socially within the society. Although our data does not provide any explicit information about such communication there is some indirect evidence that the adjustments in strategy are brought about by the increased exchange of experiences among the fishermen

Flood Forecasting Using Machine Learning Methods

This book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Wate