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A learning automata based multiobjective hyper-heuristic (2017)
Journal Article
Li, W., Özcan, E., & John, R. (2017). A learning automata based multiobjective hyper-heuristic. IEEE Transactions on Evolutionary Computation, https://doi.org/10.1109/TEVC.2017.2785346

Metaheuristics, being tailored to each particular domain by experts, have been successfully applied to many computationally hard optimisation problems. However, once implemented, their application to a new problem domain or a slight change in the pro... Read More about A learning automata based multiobjective hyper-heuristic.

Interval type-2 A-intuitionistic fuzzy logic for regression problems (2017)
Journal Article
Eyoh, I., John, R., & de Maere, G. (in press). Interval type-2 A-intuitionistic fuzzy logic for regression problems. IEEE Transactions on Fuzzy Systems, doi:10.1109/TFUZZ.2017.2775599. ISSN 1063-6706

This paper presents an approach to prediction based on a new interval type-2 Atanassov-intuitionistic fuzzy logic system (IT2AIFLS) of Takagi-Sugeno-Kang (TSK) fuzzy inference with neural network learning capability. The gradient descent (GD) algorit... Read More about Interval type-2 A-intuitionistic fuzzy logic for regression problems.

Extended Kalman filter-based learning of interval type-2 intuitionistic fuzzy logic system (2017)
Conference Proceeding
Eyoh, I., John, R., & De Maere, G. (2017). Extended Kalman filter-based learning of interval type-2 intuitionistic fuzzy logic system. In 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC). https://doi.org/10.1109/SMC.2017.8122694

Fuzzy logic systems have been extensively applied for solving many real world application problems because they are found to be universal approximators and many methods, particularly, gradient descent (GD) methods have been widely adopted for the opt... Read More about Extended Kalman filter-based learning of interval type-2 intuitionistic fuzzy logic system.

Interval type-2 defuzzification using uncertainty weights (2017)
Journal Article
Runkler, T. A., Coupland, S., John, R., & Chen, C. (in press). Interval type-2 defuzzification using uncertainty weights. Studies in Computational Intelligence, 739, https://doi.org/10.1007/978-3-319-67789-7_4

One of the most popular interval type-2 defuzzification methods is the Karnik-Mendel (KM) algorithm. Nie and Tan (NT) have proposed an approximation of the KM method that converts the interval type-2 membership functions to a single type-1 membership... Read More about Interval type-2 defuzzification using uncertainty weights.

Vehicle incident hot spots identification: an approach for big data (2017)
Conference Proceeding
Triguero, I., Figueredo, G. P., Mesgarpour, M., Garibaldi, J. M., & John, R. (2017). Vehicle incident hot spots identification: an approach for big data. https://doi.org/10.1109...igDataSE/ICESS.2017.329

In this work we introduce a fast big data approach for road incident hot spot identification using Apache Spark. We implement an existing immuno-inspired mechanism, namely SeleSup, as a series of MapReduce-like operations. SeleSup is composed of a nu... Read More about Vehicle incident hot spots identification: an approach for big data.

Machine learning and statistical approaches to classification – a case study (2017)
Conference Proceeding
Eyoh, I., & John, R. (2017). Machine learning and statistical approaches to classification – a case study

The advent of information technology has led to the proliferation of data in disparate databases. Organisations have become data rich but knowledge poor. Users need efficient analysis tools to help them understand their data, predict future trends an... Read More about Machine learning and statistical approaches to classification – a case study.

An Immune-Inspired Technique to Identify Heavy Goods Vehicles Incident Hot Spots (2017)
Journal Article
Figueredo, G. P., Triguero, I., Mesgarpour, M., Maciel Guerra, A., Garibaldi, J. M., & John, R. (2017). An Immune-Inspired Technique to Identify Heavy Goods Vehicles Incident Hot Spots. IEEE Transactions on Emerging Topics in Computational Intelligence, 1(4), 248-258. https://doi.org/10.1109/TETCI.2017.2721960

We report on the adaptation of an immune-inspired instance selection technique to solve a real-world big data problem of determining vehicle incident hot spots. The technique, which is inspired by the Immune System self-regulation mechanism, was orig... Read More about An Immune-Inspired Technique to Identify Heavy Goods Vehicles Incident Hot Spots.

A new dynamic approach for non-singleton fuzzification in noisy time-series prediction (2017)
Conference Proceeding
Pourabdollah, A., John, R., & Garibaldi, J. M. (in press). A new dynamic approach for non-singleton fuzzification in noisy time-series prediction

Non-singleton fuzzification is used to model uncertain (e.g. noisy) inputs within fuzzy logic systems. In the standard approach, assuming the fuzzification type is known, the observed [noisy] input is usually considered to be the core of the input fu... Read More about A new dynamic approach for non-singleton fuzzification in noisy time-series prediction.

Time series forecasting with interval type-2 intuitionistic fuzzy logic systems (2017)
Conference Proceeding
Eyoh, I., John, R., & de Maere, G. (in press). Time series forecasting with interval type-2 intuitionistic fuzzy logic systems

Conventional fuzzy time series approaches make use of type-1 or type-2 fuzzy models. Type-1 models with one index (membership grade) cannot fully handle the level of uncertainty inherent in many real world applications. The type-2 models with upper a... Read More about Time series forecasting with interval type-2 intuitionistic fuzzy logic systems.

Type-1 and interval type-2 ANFIS: a comparison (2017)
Conference Proceeding
Chen, C., John, R., Twycross, J., & Garibaldi, J. M. (2017). Type-1 and interval type-2 ANFIS: a comparison

In a previous paper, we proposed an extended ANFIS architecture and showed that interval type-2 ANFIS produced larger errors than type-1 ANFIS on the well-known IRIS classification problem. In this paper, more experiments on both synthetic and real-w... Read More about Type-1 and interval type-2 ANFIS: a comparison.

Similarity-based non-singleton fuzzy logic control for improved performance in UAVs (2017)
Conference Proceeding
Fu, C., Sarabakha, A., Kayacan, E., Wagner, C., John, R., & Garibaldi, J. M. (2017). Similarity-based non-singleton fuzzy logic control for improved performance in UAVs. https://doi.org/10.1109/FUZZ-IEEE.2017.8015440

© 2017 IEEE. As non-singleton fuzzy logic controllers (NSFLCs) are capable of capturing input uncertainties, they have been effectively used to control and navigate unmanned aerial vehicles (UAVs) recently. To further enhance the capability to handle... Read More about Similarity-based non-singleton fuzzy logic control for improved performance in UAVs.

Elliptic membership functions and the modeling uncertainty in type-2 fuzzy logic systems as applied to time series prediction (2017)
Conference Proceeding
Kayacan, E., Coupland, S., John, R., & Khanesar, M. A. (2017). Elliptic membership functions and the modeling uncertainty in type-2 fuzzy logic systems as applied to time series prediction. In 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). https://doi.org/10.1109/FUZZ-IEEE.2017.8015457

In this paper, our aim is to compare and contrast various ways of modeling uncertainty by using different type-2 fuzzy membership functions available in literature. In particular we focus on a novel type-2 fuzzy membership function–”Elliptic membersh... Read More about Elliptic membership functions and the modeling uncertainty in type-2 fuzzy logic systems as applied to time series prediction.

Tuning a Simulated Annealing metaheuristic for cross-domain search (2017)
Conference Proceeding
Jackson, W. G., Özcan, E., & John, R. (in press). Tuning a Simulated Annealing metaheuristic for cross-domain search

Simulated Annealing is a well known local search metaheuristic used for solving computationally hard optimization problems. Cross-domain search poses a higher level issue where a single solution method is used with minor, preferably no modification f... Read More about Tuning a Simulated Annealing metaheuristic for cross-domain search.

Learning heuristic selection using a time delay neural network for open vehicle routing (2017)
Conference Proceeding
Tyasnurita, R., Özcan, E., & John, R. (2017). Learning heuristic selection using a time delay neural network for open vehicle routing

A selection hyper-heuristic is a search method that controls a prefixed set of low-level heuristics for solving a given computationally difficult problem. This study investigates a learning-via demonstrations approach generating a selection hyper-heu... Read More about Learning heuristic selection using a time delay neural network for open vehicle routing.

A modified indicator-based evolutionary algorithm (mIBEA) (2017)
Conference Proceeding
Li, W., Özcan, E., John, R., Drake, J. H., Neumann, A., & Wagner, M. (2017). A modified indicator-based evolutionary algorithm (mIBEA)

Multi-objective evolutionary algorithms (MOEAs) based on the concept of Pareto-dominance have been successfully applied to many real-world optimisation problems. Recently, research interest has shifted towards indicator-based methods to guide the sea... Read More about A modified indicator-based evolutionary algorithm (mIBEA).

A Direct Approach for Determining the Switch Points in the Karnik–Mendel Algorithm (2017)
Journal Article
Chen, C., John, R., Twycross, J., & Garibaldi, J. M. (2018). A Direct Approach for Determining the Switch Points in the Karnik–Mendel Algorithm. IEEE Transactions on Fuzzy Systems, 26(2), 1079-1085. https://doi.org/10.1109/tfuzz.2017.2699168

The Karnik-Mendel algorithm is used to compute the centroid of interval type-2 fuzzy sets, determining the switch points needed for the lower and upper bounds of the centroid, through an iterative process. It is commonly acknowledged that there is n... Read More about A Direct Approach for Determining the Switch Points in the Karnik–Mendel Algorithm.

Type-2 fuzzy linear systems (2017)
Journal Article
Najariyan, M., Mazandarani, M., & John, R. (2017). Type-2 fuzzy linear systems. International Journal of Granular Computing, Rough Sets and Intelligent Systems, 2(3), https://doi.org/10.1007/s41066-016-0037-y

Fuzzy Linear Systems (FLSs) are used in practical situations where some of the systems parameters or variables are uncertain. To date, investigations conducted on FLSs are restricted to those in which the uncertainty is assumed to be modeled by Type-... Read More about Type-2 fuzzy linear systems.

On Nie-Tan operator and type-reduction of interval type-2 fuzzy sets (2017)
Journal Article
Li, J., John, R., Coupland, S., & Kendall, G. (2018). On Nie-Tan operator and type-reduction of interval type-2 fuzzy sets. IEEE Transactions on Fuzzy Systems, 26(2), https://doi.org/10.1109/TFUZZ.2017.2666842

Type-reduction of type-2 fuzzy sets is considered to be a defuzzification bottleneck because of the computational complexity involved in the process of type-reduction. In this research, we prove that the closed-form Nie-Tan operator, which outputs th... Read More about On Nie-Tan operator and type-reduction of interval type-2 fuzzy sets.

R-fuzzy sets and grey system theory (2017)
Conference Proceeding
Singh Khuman, A., Yang, Y., John, R., & Liu, S. (2017). R-fuzzy sets and grey system theory. In 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC). https://doi.org/10.1109/SMC.2016.7844949

This paper investigates the use of grey theory to en- hance the concept of an R-fuzzy set, with regards to the precision of the encapsulating set of returned significance values. The use of lower and upper approximations from rough set theory, allow... Read More about R-fuzzy sets and grey system theory.

Multi-objective optimisation in inventory planning with supplier selection (2017)
Journal Article
Turk, S., Özcan, E., & John, R. (2017). Multi-objective optimisation in inventory planning with supplier selection. Expert Systems with Applications, 78, https://doi.org/10.1016/j.eswa.2017.02.014

Supplier selection and inventory planning are critical and challenging tasks in Supply Chain Management. There are many studies on both topics and many solution techniques have been proposed dealing with each problem separately. In this study, we pre... Read More about Multi-objective optimisation in inventory planning with supplier selection.


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