An evolutionary approach to constraint-regularized learning

The success of machine learning methods for inducing models from data crucially depends on the proper incorporation of background knowledge about the model to be learned. The idea of constraint-regularized learning is to em- ploy fuzzy set-based modeling techniques in order to express such knowl- edge in a flexible way, and to formalize it in terms of fuzzy constraints. Thus, background knowledge can be used to appropriately bias the learn- ing process within the regularization framework of inductive inference. After a brief review of this idea, the paper offers an operationalization of constraint- regularized learning. The corresponding framework is based on evolutionary methods for model optimization and employs fuzzy rule bases of the Takagi- Sugeno type as flexible function approximators

Tuning Genetic Algorithm Parameters to Improve Convergence Time

Fermentation processes by nature are complex, time-varying, and highly nonlinear. As dynamic systems their modeling and further high-quality control are a serious challenge. The conventional optimization methods cannot overcome the fermentation processes peculiarities and do not lead to a satisfying solution. As an alternative, genetic algorithms as a stochastic global optimization method can be applied. For the purpose of parameter identification of a fed-batch cultivation of S. cerevisiae altogether four kinds of simple and four kinds of multipopulation genetic algorithms have been considered. Each of them is characterized with a different sequence of implementation of main genetic operators, namely, selection, crossover, and mutation. The influence of the most important genetic algorithm parameters-generation gap, crossover, and mutation rates has-been investigated too. Among the considered genetic algorithm parameters, generation gap influences most significantly the algorithm convergence time, saving up to 40% of time without affecting the model accuracy

Hybridizing genetic algorithms with sharing scheme and evolution strategies for designing approximate fuzzy rule-based systems

Genetic algorithms and evolution strategies are combined in order to build a multi-stage hybrid evolutionary algorithm for learning constrained approximate Mamdani-type knowledge bases from examples. The genetic algorithm niche concept is used in two of the three stages composing the learning process with the purpose of improving the accuracy of the designed fuzzy rule-based systems. The proposed genetic fuzzy rule-based system is used to solve an electrical engineering problem and the results obtained are compared with other methods presenting different characteristics

Hybridizing Genetic Algorithms with Sharing Scheme and Evolution Strategies for Designing Approximate Fuzzy Rule-Based Systems

Genetic Algorithms and Evolution Strategies are combined in order to build a multistage Hybrid Evolutionary Algorithm for learning constrained Approximate Mamdani-type Knowledge Bases from examples. The Genetic Algorithm niche concept is used in two of the three stages composing the learning process with the purpose of improving the accuracy of the designed Fuzzy Rule-Based Systems. The proposed Genetic Fuzzy Rule-Based System is used to solve an Electrical Engineering problem and the results obtained are compared with other methods presenting different characteristics

Application of Simulation Modelling to Machine Breakdown

Industrial technology has excelled profoundly in the past few decades, helping organisations throughout the world to be more efficient in all processes and keeping costs down. However, despite the abundance of several IT solutions, there exist many problems where more than one decision has to be made. Among the techniques supporting a multi-decisional context, simulations can undoubtedly play an important role as they provide what-if analysis and hence help to evaluate quantitative benefits. This thesis develops a simulation model for breakdown in an industrial machine, the main crusher in a cement factory. It also examines three important parameters (Drill Head, Dusting and Lubrication) of the crusher machine with the use of Bayesian network modelling which allows determination of suitable influencing factors in a precise and dynamic manner. The model also supports integration with management systems such as J.I.T, and MRPII. Witness simulation software has been used in this work to model the breakdown frequency of the Crusher machine and the associated parameters. The Bayesian Network Modelling is used to consider historical data and expert opinions; the Bayes’ approach takes into consideration off all existing parameters that affect the machine breakdown directly or indirectly. This tool is capable of establishing a probability based on the information gathered about the parameters. The simulation model is developed further to enable the Bayesian Network Modelling to be applied via the Chain Rule to calculate the probability of failure. The findings of this research show the approach developed in this work, where the Bayesian probability development process is integrated into the simulation model. This provides a unique and dynamic tool to aid decision making in understanding machine breakdowns. The resulting simulator is a decision making tool capable of analysing the status of the machine and the associated influencing factors. This uses an approach based on multiple performance measures and a user-defined set of inputs based on historical and expert opinion. This work provides a methodology to study the importance of key parameters of the crusher machine. This in effect highlights the correlation between the governing parameters and the occurrence of breakdown

Focusing On Interpretability And Accuracy Of A Genetic Fuzzy System

This research work presents a new approach for fuzzy system building taking into account the accuracy and interpretability of the system. One difficulty in the handling of high-dimensional problems by fuzzy rule-based systems is the exponential increase in the number of rules and in the number of conditions in the antecedent part of the rule. Thus, as first step of the proposed approach we apply a feature selection process in order to exclude irrelevant variables. Besides that, dimensionality reduction generally promotes the accuracy and comprehensibility of the system. After that, a genetic algorithm is used for deriving short and comprehensible fuzzy rules. Finally another genetic algorithm is used for optimizing the rule base obtained in the last step, excluding unnecessary and redundant rules. The fitness function of the algorithms consider both accuracy and interpretability of the fuzzy model and the use of "don't care" condition allows to generate more comprehensible with high generalization capacity. The application domain is multidimensional fuzzy pattern classification. By computational simulation in some well-know datasets, we can see that the proposed approach is able to generate compact fuzzy rule bases with high classification ability. 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