Analysis of the niche genetic algorithm in learning classifier systems

We propose a new framework of computing-inspired multifocal cancer detection procedure (MCDP). Under the rubric of MCDP, the tumor foci to be detected are regarded as solutions of the objective function, the tissue region around the cancer areas represents the parameter space, and the nanorobots loaded with contrast medium molecules for cancer detection correspond to the optimization agents. The process that the nanorobots detect tumors by swimming in the high-risk tissue region can be regarded as the process that the agents search for the solutions of an objective function in the parameter space with some constraints. For multimodal optimization (MMO) aiming to locate multiple optimal solutions in a single simulation run, the niche technology has been widely used. Specifically, the niche genetic algorithm (NGA) has been shown to be particularly effective in solving MMO. It can be used to identify the global optima of multiple hump functions in a running, effectively keep the diversity of the population, and prematurely avoid the genetic algorithm. Learning from the optimization procedure of NGA, we propose the NGA-inspired MCDP in order to locate the tumor targets efficiently while taking into account realistic in vivo propagation and controlling of nanorobots, which is different from the use scenario of the standard NGA. To improve the performance of the MCDP, we also modify the crossover operator of the original NGA from crossing within a population to crossing between two populations. Finally, we present comprehensive numerical examples to demonstrate the effectiveness of the NGA-inspired MCDP when the biological objective function is associated with the blood flow velocity profile caused by tumor-induced angiogenesis.

Public landscape is a settlement landscape formed under human intervention, which is both cultural resources and ecological environment resources. Niche genetic algorithm is the most widely used algorithm in modern times. It greatly expands the space of system design and optimizes all links in public landscape environment optimization system design. This emerging technology also meets a wide range of public landscape needs. Starting from the influence of environmental factors and niche genetic algorithm, this paper discusses various links of modern public landscape environmental design in urban environment, deeply discusses the relationship and background between niche genetic algorithm and optimizing public landscape environmental design system, and analyzes the limitations of public landscape design at present, Combined with the research of niche genetic algorithm at home and abroad, this paper analyzes the performance links and characteristics of public landscape environment design system, focuses on its application principles and aesthetic evaluation in improving the efficiency of public landscape environment optimization system, and analyzes and discusses the advantages of this emerging tool in the use of public landscape environment optimization system.

It was demonstrated that a niching genetic algorithm (NGA) could be efficient for the optimization of a SiC crystal growth system. And several design parameters of SiC crystal growth system could be optimized at the same time, and high diversity of population was maintained to obtain global optimization solution by NGA. Firstly, the NGA and thermal models were described and applied for SiC crystal growth by physical vapor transport (PVT) method, and the combination method of NGA and thermal models were presented. Then two cases were carried out to demonstrate the automatic optimization of SiC crystal growth system by NGA. One case was a single-objective optimization problem, in which the axial position of coils was optimized to improve the growth rate of crystal. The another was a multi-objective optimization problem, in which the thickness of substrate holder and input current were optimized for uniform temperature distribution along the growth surface for reducing the thermal stresses in growing crystal. Finally, all the optimization results were analyzed.

This issue is dedicated to Stewart Wilson both in recognition of his achievements and in appreciation for his guidance of the field. Not only has his research transformed the field, but his support, encouragement, and advice have been invaluable to many of us individually and to the community as a whole. Learning Classifier Systems (LCS) are rule-based machine learning algorithms introduced by Holland (see [2, 4–6]) that learn a population of IF-THEN rules that specify ‘‘IF x happens THEN do (or predict) y’’. In 1995 Stewart Wilson, already a leading LCS researcher, published a paper entitled ‘‘Classifier Fitness based on Accuracy’’ [16] that introduced the XCS algorithm. This paper proved a turning point for the field as XCS and its derivatives rapidly became the main focus of LCS research, and continue to be so today. We organised this special issue of Evolutionary Intelligence to mark the 20th anniversary of this landmark paper, and to serve as post-proceedings for IWLCS 2014: the Seventeenth International Workshop on Learning Classifier Systems. The annual IWLCS meetings are the yearly highlight of the LCS calendar and have no doubt contributed to the strong sense of community in the LCS field. For 2015 IWLCS has been rebranded as the more descriptive IWERML: the International Workshop on Evolutionary Rule-based Machine Learning. In 1994, the year before introducing XCS, Wilson introduced ZCS, the ‘‘Zeroth-order Classifier System’’ [15]. Wilson felt the growing complexity of the LCS concept was hindering progress and ZCS was an attempt to strip the LCS down to its essentials while retaining a working system. XCS built on the minimalist ZCS with two radical changes: a switch to accuracy-based fitness and, based on work by Booker [1], the addition of a niche Genetic Algorithm (GA) that strongly favours general rules. This combination was a hit: the niche GA favours general rules but accuracy-based fitness insists strictly that they make good predictions. This combination drives XCS to learn rules that are as general as possible while remaining accurate. Bull’s article in this issue provides much more on the history of LCS prior to and following XCS [2]. The famous ‘‘accuracy-based fitness’’ of XCS needs some explanation. The earlier ZCS featured ‘‘strengthbased fitness’’, in which the fitness of a rule in the GA was derived from its strength, a measure of the amount of reward the rule received. Consequently, fit rules in ZCS are those that receive a lot of reward. In contrast, XCS rules are fit if they make consistently accurate predictions about the reward they receive. This has the counterintuitive consequence that XCS rules that consistently take a bad action can be fit, since they are consistent. However, this poses no problem when it comes time for XCS to choose an action, since that can be done using the magnitude of the reward predicted by each rule. Why does XCS retain rules whose action it does not use? This ‘‘complete map’’ of the state/ & Muhammad Iqbal muhammad.iqbal@ecs.vuw.ac.nz

Based on the study of the unidirectional loop layout, this paper analyzed the necessity of genetic algorithm to the workshop layout optimization. To minimize transportation travel, the mathematical model of the unidirectional loop workshop facility layout was set up, using the niche genetic algorithm with introduction of penalty function and elite reservation strategy to accomplish the facility layout optimization. Through the calculation of the example, the feasibility and effectiveness of this algorithm in facility layout optimization design is verified.

In order to optimize the orthogonal design of MIMO radar waveform, an improved algorithm based on niche genetic algorithm is proposed. The algorithm is able to eliminate individuals with similar structures in the population or data, maintain a certain distance between individuals, and effectively hold the diversity of them It thus eliminates the phenomenon of premature convergence, strengthens the global search ability of the algorithm, and improves its convergence speed. It is shown that the algorithm has both the advantages of overall search ability of genetic algorithm and the ability of the niche genetic algorithm to discover multiple optimal solutions. Simulation was given to show that the algorithm was cable to reduce the side-lobe of the auto-correlation function of the orthogonal polyphase code and the peak value of the cross-correlation function of the code with good performance of autocorrelation and cross-correlation obtained

In this paper we calculate a synthetic medium surface displacement response that is consistent with real measurement data by applying the least-square principle and a niche genetic algorithm to the parameters inversion problem of the wave equation in a two-phase medium. We propose a niche genetic multi-parameter (including porosity, solid phase density and fluid phase density) joint inversion algorithm based on a two-phase fractured medium in the BISQ model. We take the two-phase fractured medium of the BISQ model in a two-dimensional half space as an example, and carry out the numerical reservoir parameters inversion. Results show that this method is very convenient for solving the parameters inversion problem for the wave equation in a two-phase medium, and has the advantage of strong noise rejection. Relative to conventional genetic algorithms, the niche genetic algorithm based on a sharing function can not only significantly speed up the convergence, but also improve the inversion precision.

In this paper, cluster splitting and merging algorithms are used for flood assessment using LISS-III (before flood) and SAR (during flood) images. Bayesian Information Criteria (BIC) is used to determine the optimal number of clusters. Keeping this constraint, the cluster centers are generated using the cluster splitting techniques, namely Mean Shift Clustering (MSC), and Niche Genetic Algorithm (NGA). The merging method is used to group the data points into their respective classes, using the cluster centers obtained from the above techniques. These techniques are applied on the LISS-III and SAR image. Further, the resultant images are overlaid to analyze the extent of the flood in individual land classes. A performance comparison of these techniques (MSC and NGA) is presented. From the results obtained, we deduce that the NGA is efficient.

A learning classifier system is one of the methods for applying a genetic-based approach to machine learning applications. An enhanced version of the system that employs the Bucket-brigade algorithm to reward individuals in a chain of co-operating rules is implemented and assigned the task of learning rules for control robotics behaviors. Illustrates the approach with the example of kicking a moving Ball into a goal (KMB). The KMB (kick a Moving Ball) System built of two-classifier subsystems work together, each classifier system learns a simple behavior, first classifier system, learn simulated robot chase behavior i.e. learn robot to move single step toward moving ball, second classifier system, learn the simulated robot approach behavior i.e. learn robot to kick the ball toward fixed goal, the system as a whole has as its learning goal the coordinate of behaviors. This work examine the performance of the simple classifier system (SCS) on the (KMB) problem, perform two SCS simulations one without the genetic algorithm enabled (GA) and one with the genetic algorithm enabled (GA) results using a classifier system with genetic algorithm show improvement over one without, and furthermore the level of performance has been high enough to rival human accuracy. Also the results without the genetic algorithm show that the apportionment of credit algorithm adjust the strength values of the rules.

This paper presents a crowding niche cellular genetic algorithm (referred to NCGA) aiming at solving the problems of local convergence and non-uniform population distribution in traditional genetic algorithm. The selecting operation in traditional genetic algorithm is improved by bringing in the concept of neighbors of cellular genetic algorithm, and the population distribution is greatly enhanced by introducing crowding niche mechanism, which betters the ability of global searching and helps to avoid the population local convergence. Meanwhile, the paper describes the crowding niche cellular genetic algorithm in details and compares it with simple genetic algorithm （SGA） and simple niche genetic algorithm （NGA）; the comparison results reveal that, NCGA outperforms the other two algorithms in terms of convergence rate and population diversity.

Three different optimization methods using genetic algorithms have been developed, aiming to achieve better aggressive intermediate turbine duct (ITD) performance. To overcome defects of simple genetic algorithms, a niche genetic algorithm is used, for its better adaptability to multi-peak function. These three methods are two-dimensional optimization for pursuing the highest static pressure coefficient; two-dimensional optimization via controlling static pressure coefficient; and a further three-dimensional optimization. The second method introduces a restrainer to make sure the maximum value of static pressure coefficient gradient less than a limitation. A simulation case, an ITD with eight struts, was implemented to demonstrate the capabilities of the presented optimization methods. Compared to the baseline ITD, the results show as follow. The first method obtains a best static pressure coefficient but a severe separation. The second optimization method with static pressure coefficient gradient control can definitely suppress separation in the ITD, the second method also obtains better static pressure coefficient and the lowest total pressure loss coefficient. The third method, a further three-dimensional optimization can obtain better ITD overall performance because of a more realistic simulation. Nevertheless, the second two-dimensional optimization method can get good enough results while it is apparently much more time-saving compared to three-dimensional one, which make it more suitable for engineering applications.

Because of the complexity of gear working condition, there are non-linear relationship between characteristic parameters and fault types. This paper proposes to apply the artificial neural network theory and the genetic algorithm to solve the difficulties of gear fault diagnosis. Niche technique based on crowding mechanism is used in genetic algorithm, and punishing function is adopted to adjust individual fitness, so as to promote global search capability. Taking a certain gearbox fault signal acquisition experimental system for an example, Matlab software and its neural network toolbox are used to model and simulate. The experiment result shows that the founded network model has good performance for the common gear fault diagnosis and it can identify various types of faults stably and accurately.

With the rapid development of economy, numbers of power grid projects are multiplying yearly. Power supply companies are more and more concerned about how to plan these projects. In order to optimize the power grid project portfolio objectively and comprehensively, a scientific indicator system is constructed by analyzing the characteristics of power grid projects. The valuation of each project is evaluated accurately on the basis of analytic hierarchy process with the indicator system. Many additional factors need to be considered in process of investment portfolio optimization of power grid project, such as the limited total funds and the limited allocated funds of subsidiaries. So niche genetic algorithm is adopt to optimize the model and obtain satisfied solution of investment portfolio of power grid projects. Experiment results show that the performance with this approach is more promising and it can make the project investment portfolio more reasonable and practical.

To solve the fatigue damage location problem of helicopter moving component, a new approach for linear location of acoustic emission (AE) source based on least squares support vector machine for regression (LS-SVR) and niche genetic algorithm (NGA) was proposed. Several time domain parameters of AE signal were taken as the inputs, and the linear coordinates of the breakpoints as the output. The sharing function based niche genetic algorithm is used to select the LS-SVR parameters automatically. The results of pencil lead break location experiment on specimen of carbon fiber materials indicate that the proposed approach can implement linear location of AE source effectively, and has better performance on convergence rate and location accuracy than RBF and BP neural network.

At present, the evolution cryptanalysis methods mainly adopt the basic genetic algorithm (literature (1) and (2)) which has early phenomenon, searching stagnation in the near optimal solution, and low calculation efficiency. This paper inspired by biological genetic evolution proposes a niche genetic algorithm based on the code analysis method. This algorithm can keep the population diversity and prevent premature convergence. In genetic operator operation, this algorithm uses (μ + λ ) selection strategy which has the strongest selection pressure. In order to achieve compensating the shortage of groups diversity easy loss, improve the searching efficiency of algorithm, speed up the convergence speed, this algorithm applies the multi-point crossover operation, at the same time adopts variation operation by introducing evolution mutation probability. This p aper chooses examples to demonstrate the replace cryptogram. Experiments show that the algorithm can effectively reduce the complexity of the problem analysis, reduce the redundant computation, speed up the convergence. Therefore, in the limited time, it can effectively get an optimal solution or suboptimal solution of the analysis object.