Long Term Memory Assistance for Evolutionary Algorithms

Bat4CEP: a bat algorithm for mining of complex event processing rules

Complex Event Processing (CEP) is a modern software technology for the dynamic analysis of continuous data streams. CEP is able of searching extremely large data streams in real time for the presence of event patterns. So far, specifying event patterns of CEP rules is still a manual task based on the expertise of domain experts. This paper presents a novel bat-inspired swarm algorithm for automatically mining CEP rule patterns that express the relevant causal and temporal relations hidden in data streams. The basic suitability and performance of the approach is proven by extensive evaluation with both synthetically generated data and real data from the traffic domain.

An Enhancing Differential Evolution Algorithm with a Rank-Up Selection: RUSDE

Recently, the differential evolution (DE) algorithm has been widely used to solve many practical problems. However, DE may suffer from stagnation problems in the iteration process. Thus, we propose an enhancing differential evolution with a rank-up selection, named RUSDE. First, the rank-up individuals in the current population are selected and stored into a new archive; second, a debating mutation strategy is adopted in terms of the updating status of the current population to decide the parent’s selection. Both of the two methods can improve the performance of DE. We conducted numerical experiments based on various functions from CEC 2014, where the results demonstrated excellent performance of this algorithm. Furthermore, this algorithm is applied to the real-world optimization problem of the four-bar linkages, where the results show that the performance of RUSDE is better than other algorithms.

Integration of Second-Order Sensitivity Method and CoKriging Surrogate Model

The global exploring feature of the surrogate model makes it a useful intermedia for design optimization. The accuracy of the surrogate model is closely related with the efficiency of optima-search. The cokriging approach described in present studies can significantly improve the surrogate model accuracy and cut down the turnaround time spent on the modeling process. Compared to the universal Kriging method, the cokriging method interpolates not only the sampling data, but also on their associated derivatives. However, the derivatives, especially high order ones, are too computationally costly to be easily affordable, forming a bottleneck for the application of derivative enhanced methods. Based on the sensitivity analysis of Navier–Stokes equations, current study introduces a low-cost method to compute the high-order derivatives, making high order derivatives enhanced cokriging modeling practically achievable. For a methodological illustration, second-order derivatives of regression model and correlation models are proposed. A second-order derivative enhanced cokriging model-based optimization tool was developed and tested on the optimal design of an automotive engine cooling fan. This approach improves the modern optimal design efficiency and proposes a novel direction for the large scale optimization problems.

The Use of Evolutionary Methods for the Determination of a DC Motor and Drive Parameters Based on the Current and Angular Speed Response

Determination of the seven parameters of a Direct Current (DC) motor and drive is presented, based on the speed and current step responses. The method is extended for the motor and drive parameter determination in the case of a controlled drive. The influence of a speed controller on the responses is considered in the motor model with the use of the measured voltage. Current limitation of the supply unit is also considered in the DC motor model. For parameter determination, a motor model is used, which is determined with two coupled differential equations. Euler’s first-order and Runge–Kutta fourth-order methods are used for the motor model simulations. For parameter determination, evolutionary methods are used and compared to each other. Methods used are Genetic Algorithm, Differential Evolutions with two strategies, Teaching–Learning-Based Optimization, and Artificial Bee Colony. To improve results, deviation of the motor model simulation time is used and Memory Assistance with three different approaches is analyzed to shorten the calculation time. The tests showed that Differential Evolution (DE)/rand/1/exp is the most appropriate for the presented problem. The division of the motor model simulation time improves the results. For the presented problem, short-term memory assistance can be suggested for calculation time reduction.

Optimizing the Estimation of a Histogram-Bin Width—Application to the Multivariate Mixture-Model Estimation

A maximum-likelihood estimation of a multivariate mixture model’s parameters is a difficult problem. One approach is to combine the REBMIX and EM algorithms. However, the REBMIX algorithm requires the use of histogram estimation, which is the most rudimentary approach to an empirical density estimation and has many drawbacks. Nevertheless, because of its simplicity, it is still one of the most commonly used techniques. The main problem is to estimate the optimum histogram-bin width, which is usually set by the number of non-overlapping, regularly spaced bins. For univariate problems it is usually denoted by an integer value; i.e., the number of bins. However, for multivariate problems, in order to obtain a histogram estimation, a regular grid must be formed. Thus, to obtain the optimum histogram estimation, an integer-optimization problem must be solved. The aim is therefore the estimation of optimum histogram binning, alone and in application to the mixture model parameter estimation with the REBMIX&EM strategy. As an estimator, the Knuth rule was used. For the optimization algorithm, a derivative based on the coordinate-descent optimization was composed. These proposals yielded promising results. The optimization algorithm was efficient and the results were accurate. When applied to the multivariate, Gaussian-mixture-model parameter estimation, the results were competitive. All the improvements were implemented in the rebmix R package.

From Grammar Inference to Semantic Inference—An Evolutionary Approach

This paper describes a research work on Semantic Inference, which can be regarded as an extension of Grammar Inference. The main task of Grammar Inference is to induce a grammatical structure from a set of positive samples (programs), which can sometimes also be accompanied by a set of negative samples. Successfully applying Grammar Inference can result only in identifying the correct syntax of a language. With the Semantic Inference a further step is realised, namely, towards inducing language semantics. When syntax and semantics can be inferred, a complete compiler/interpreter can be generated solely from samples. In this work Evolutionary Computation was employed to explore and exploit the enormous search space that appears in Semantic Inference. For the purpose of this research work the tool LISA.SI has been developed on the top of the compiler/interpreter generator tool LISA. The first results are encouraging, since we were able to infer the semantics only from samples and their associated meanings for several simple languages, including the Robot language.