Evolutionary optimization using equitable fuzzy sorting genetic algorithm (EFSGA)

https://ieeexplore.ieee.org/document/8598717This paper presents a fuzzy dominance-based analytical sorting method as an advancement to the existing multi-objective evolutionary algorithms (MOEA). Evolutionary algorithms (EAs), on account of their sorting schemes, may not establish clear discrimination amongst solutions while solving many-objective optimization problems. Moreover, these algorithms are also criticized for issues such as uncertain termination criterion and difficulty in selecting a final solution from the set of Pareto optimal solutions for practical purposes. An alternate approach, referred here as equitable fuzzy sorting genetic algorithm (EFSGA), is proposed in this paper to address these vital issues. Objective functions are defined as fuzzy objectives and competing solutions are provided an overall activation score (OAS) based on their respective fuzzy objective values. Subsequently, OAS is used to assign an explicit fuzzy dominance ranking to these solutions for improved sorting process. Benchmark optimization problems, used as case studies, are optimized using proposed algorithm with three other prevailing methods. Performance indices are obtained to evaluate various aspects of the proposed algorithm and present a comparison with existing methods. It is shown that the EFSGA exhibits strong discrimination ability and provides unambiguous termination criterion. The proposed approach can also help user in selecting final solution from the set of Pareto optimal solutions

Analytics of Heterogeneous Breast Cancer Data Using Neuroevolution

https://ieeexplore.ieee.org/document/8632897Breast cancer prognostic modeling is difficult since it is governed by many diverse factors. Given the low median survival and large scale breast cancer data, which comes from high throughput technology, the accurate and reliable prognosis of breast cancer is becoming increasingly difficult. While accurate and timely prognosis may save many patients from going through painful and expensive treatments, it may also help oncologists in managing the disease more efficiently and effectively. Data analytics augmented by machine-learning algorithms have been proposed in past for breast cancer prognosis; and however, most of these could not perform well owing to the heterogeneous nature of available data and model interpretability related issues. A robust prognostic modeling approach is proposed here whereby a Pareto optimal set of deep neural networks (DNNs) exhibiting equally good performance metrics is obtained. The set of DNNs is initialized and their hyperparameters are optimized using the evolutionary algorithm, NSGAIII. The final DNN model is selected from the Pareto optimal set of many DNNs using a fuzzy inferencing approach. Contrary to using DNNs as the black box, the proposed scheme allows understanding how various performance metrics (such as accuracy, sensitivity, F1, and so on) change with changes in hyperparameters. This enhanced interpretability can be further used to improve or modify the behavior of DNNs. The heterogeneous breast cancer database requires preprocessing for better interpretation of categorical variables in order to improve prognosis from classifiers. Furthermore, we propose to use a neural network-based entity-embedding method for categorical features with high cardinality. This approach can provide a vector representation of categorical features in multidimensional space with enhanced interpretability. It is shown with evidence that DNNs optimized using evolutionary algorithms exhibit improved performance over other classifiers mentioned in this paper

PERFORMANCE OPTIMIZATION OF NEUROEVOLUTION FOR IMPROVED PROGNOSIS OF THE BREAST CANCER

Cancer is the second largest cause of mortality, responsible for one in every six deaths globally. Cancer has a significant socio-economic impact and its global cost is estimated to be close to $150 billion. Breast cancer is the most common female cancer and its high incidence places it among Kazakhstan’s most challenging public health problems. Advances in computing and sensing technologies and increased storage availability means that vast quantities of data are now available. While the data is sure to help practitioners understand what causes breast cancer and the best treatment approaches, the number of oncologists understanding its use is limited. Accurate and reliable prognoses are increasingly difficult because of the enormous amounts of data about breast cancer and the low survival rates. The available data’s heterogeneity adds to the challenges for data analytics posed by sheer data volume. Moreover, categorical variables in the heterogeneous dataset require accurate pre-processing if enhanced interpretation is to make progress towards prognosis possible. An advanced research in estimating the missing values in databases is also introduced in this thesis work. Rigorous research efforts have brought about the development of a novel entity embedding scheme based on neural networks capable of addressing effectively the encoding of categorical variables with high cardinality during the presented research. Employing our proposed scheme, it is now possible to represent the categorical variables as real values in high-dimensional space capable of greatly improved interpretation. Neuroevolution, which is a Meta heuristic approach, has been suggested through our work as a robust way of modelling prognosis from the breast cancer database. Neuroevolution also results in multiple equitable solutions of DNNs (Deep Neural Networks) thereby providing users with many options to choose from. Neuroevolution performance has been optimized using the EAs (Evolutionary Algorithms), namely, MOEA/D, NSGAIII, and SPEA2, but this research revealed a number of limitations in existing EAs and so this thesis proposes an improved EA: FIEA (Fuzzy Inspired Evolutionary Algorithm) which uses a fuzzy analytical approach to perform multi-criteria optimization and is also instrumental in selecting a final DNN model from the Pareto optimal set. This approach also provides insight into how the hyper-parameters control accuracy, sensitivity, F1 and other performance metrics. This is a change from traditional approaches which apply DNNs as a black box. The interpretability improved in this way can be used to advance or adjust DNNs’ behaviour and there is evidence that FIEA-optimized DNNs perform better than other algorithms described in the literature