Grammatical Evolution Using Fireworks Algorithm

This paper presents a hard clustering technique using fireworks algorithm with adaptive transfer function (FWAATF) for image segmentation. The fireworks algorithm (FWA) is a recently developed new Swarm Intelligence (SI) algorithm for function optimization. This algorithm simulates the process of fireworks explosion in the night sky. The main characteristic of FWA is the good balance between exploration and exploitation during the search process. The exploitation is done using good fireworks whereas the bad fireworks are responsible for exploration. FWA shows its efficiency and effectiveness in numerical function optimization over other SI algorithm like particle swarm optimization (PSO). FWA-ATF is a modified version of basic FWA and in this work, it is used in hard clustering technique to segment the image. FWA-ATF is used to find the optimal cluster centroids corresponding to different regions in the image. The proposed clustering technique is applied to segment four benchmark images and the well-known cluster validity index-Dunn's Index is used to measure the performance of the proposed clustering technique quantitatively. The performance of the proposed method is compared with clustering using K-means, PSO and basic FWA. The experimental results demonstrates that the proposed clustering technique with FWA-ATF performs better than other methods in segmentation for most of the images.

This paper shows how attribute grammar (AG) can be used with Grammatical Evolution (GE) to avoid invalidators in the symbolic regression solutions generated by GE. In this paper, we also show how interval arithmetic can be implemented with AG to avoid selection of certain arithmetic operators or transcendental functions, whenever necessary to avoid infinite output bounds in the solutions. Results and analysis demonstrate that with the proposed extensions, GE shows significantly less overfitting than standard GE and Koza's GP, on the tested symbolic regression problems.

A massively parallel Grammatical Evolution technique with OpenCL

Peak-to-Average Power Ratio Reduction Using New Swarm Intelligence Algorithm in OFDM Systems

This chapter evaluates the performance of various methods to constant creation in Grammatical Evolution (GE), and validates the results by comparing against those from a reasonably standard Genetic Programming (GP) setup. Specifically, the chapter compares a standard GE method to constant creation termed digit concatenation with what this chapter calls compact methods to constant creation. Constant creation in GE is an important issue due to the disruptive nature of ripple crossover, which can radically remap multiple terminals in an individual, and we investigate if more compact methods, which are more similar to the GP style of constant creation (Ephemeral Random Constants (ERCs), perform better. The results are surprising. Against common wisdom, a standard GE approach of digit concatenation does not produce individuals that are any larger than those from methods which are designed to use less genetic material. In fact, while GP characteristically evolves increasingly larger individuals, GE—after an initial growth or drop in sizes—tends to keep individual sizes stable despite no explicit mechanisms to control size growth. Furthermore, various GE setups perform acceptably well on unseen test data and typically outperform GP. Overall, these results encourage a belief that standard GE methods to symbolic regression are relatively resistant to pathogenic evolutionary tendencies of code bloat and overfitting.

Fireworks algorithm is a novel swarm intelligence algorithm for solving optimization problems - the latest versions include the adaptive fireworks algorithm and the dynamic fireworks algorithm. However, the mutation operator in the former algorithm was ineffective, whereas there was no mutation operator available in the latter algorithm. In this paper, a mutation operator is proposed, dubbed as the covariance mutation (CM) operator. The CM operator utilizes the information of the sparks with better fitness values to generate potential sparks for finding the optima of functions with higher possibility. Therefore, we proposed the fireworks algorithm with covariance mutation (FWACM) and compared it with the most advanced fireworks algorithms. The experimental results show that FWACM is a significant improvement for fireworks algorithms.

Data set preprocessing is a critical step for the successful application of machine learning algorithms in classification tasks. Even though we rely on learning algorithms to pinpoint the optimal decision boundaries in the feature space by properly detecting latent relationships among the input features, their performance is often bounded by the discriminative power of the available features. Therefore, much effort has been devoted to developing preprocessing methods that are capable of transforming the input data with the final goal of aiding the machine learning algorithm in building high-quality classification models. One such a method is feature construction, which is a flexible preprocessing procedure that exploits linear and nonlinear transformations of the original feature space in an attempt to capture useful information that is not explicit in the original data. Since the task of feature construction can be modelled as a heuristic search in the space of novel latent features, this paper investigates an evolutionary approach for performing such a task, namely grammatical evolution (GE). In our proposed approach, GE is employed for building an extra novel feature from the available input data in order to maximize the predictive performance of the learning algorithm in training data. Results show that many interesting implicit relationships are indeed found by the evolutionary approach, improving the performance of two well-known decision-tree induction algorithms.

In the previous Chapter we highlighted the fact that very little research has been conducted into the area of Genetic Programming (GP) in dynamic environments. In this book we outline the foundations of research to date with Grammatical Evolution (GE) for these kinds of non-stationary environments. As described earlier, GE possesses a number of features that differentiate it significantly from GP and it is these features that present the most interesting avenues for exploration in relation to dynamic environments, more so than in their application to static problems.In this chapter we start out by detailing in Section 4.1 the very first steps which we have taken with GE into the domain of non-stationary environments. Following this, in Section 4.2, we discuss the potential strengths of GE for the challenges presented by a dynamic world. Finally outline in Section 4.3 how we build the foundations upon which GE can be developed for application in these formidable environments.KeywordsGenetic ProgrammingGenetic CodeDynamic EnvironmentNeutral NetworkEvolutionary SearchThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Test specifications formats (TSFs) to facilitate standardization of testing information on ICs and circuit boards are examined. The author describes the application areas of a TSF and shows the practical use of one particular TSF, the neutral code format (NCF) proposed by C. Mortensen (1987). By writing a translator from NCF to a test programming language, it was seen that automatic test program generation is possible. It is found, however, that only GO/NOGO test programs could be generated.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

An algorithm called the O-algorithm is introduced for automatic test program generation of microprocessors in a user environment. To eliminate redundant tests, a weight-digraph model is used to model the signal flow of the general microprocessors. Improved functional fault models of microprocessors are derived from the Turing machine model. The O-algorithm is then constructed on the basis of the signal flow model and functional fault models. Simulation has shown that the fault coverage is better than 97%.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The paper develops a methodology for the automatic test program generation (ATPG) for a large analogue circuit subject to component drifts. An optimization algorithm developed selects the best set of intermediate access points for a.c. and d.c. tests of the circuit.During ATPG, faults are simulated by varying each component over a wide range. Voltages measured at all available nodes are thus used in the pre-processing stage of node selection. An optimization procedure based on the discriminating power of measurements and separability between fault signatures is used to select the best set of frequencies applicable for all nodes in a.c. testing. A preamplifier and control amplifier of 56 components is chosen as Unit Under Test (UUT) to demonstrate the ATPG. The fault isolation scheme used is based on the nearest neighbour rule.The rexlationship between diagnosability and number of test features is shown to follow the customary Pareto type curve. However, four carefully chosen nodes and three carefully chosen test frequencies are shown to give an adequate level of diagnosability. The whole scheme is implemented automatically using a minicomputer interfaced to the UUT.

One cause for excitement about agent oriented AI is the belief that it is easier to write programs for many narrow, autonomous systems than for fewer, broader, user-driven systems. A related cause for excitement is the primary message of these comments: it is also easier to automatically generate agent programs. The artificial life, machine learning, and automatic programming literature already describes many experiments in which systems with agent-like properties have been automatically generated. Although most of the previously generated systems have been too simple to perform helpful tasks for actual users, recent advances suggest that useful intelligent agents will soon be within reach of these techniques. The paper focuses on one particular technology, genetic programming, but related points should apply to other automatic programming technologies.

Performing a linear regression on the outputs of arbitrary symbolic expressions has empirically been found to provide great benefits. Here some basic theoretical results of linear regression are reviewed on their applicability for use in symbolic regression. It will be proven that the use of a scaled error measure, in which the error is calculated after scaling, is expected to perform better than its unscaled counterpart on all possible symbolic regression problems. As the method (i) does not introduce additional parameters to a symbolic regression run, (ii) is guaranteed to improve results on most symbolic regression problems (and is not worse on any other problem), and (iii) has a well-defined upper bound on the error, scaled squared error is an ideal candidate to become the standard error measure for practical applications of symbolic regression.

Grammatical Evolution (GE) is a grammar-based GA which generates computer programs. GE has the distinction that its input is a BNF, which permits it to generate programs in any language, of arbitrary complexity. Part of the power of GE is that it is closer to natural DNA than other Evolutionary Algorithms, and thus can benefit from natural phenomena such as a separation of search and solution spaces through a genotype to phenotype mapping, and a genetic code degeneracy which can give rise to silent mutations that have no effect on the phenotype. It has previously been shown how runs of GE are competitive with GP, and in this paper we analyse the feature of genetic code degeneracy, and its implications for genotypic diversity. Results show that genetic diversity is improved as a result of degeneracy in the genetic code for the problem domains addressed here.

In this paper, we propose a scheme for evolving multiple-input-multiple-output (MIMO) artificial neural networks (ANNs) using grammatical evolution (GE). GE is a well-known technique for program evolution. While it has also been used for the evolution of ANN structures in the past, little work is reported on the evolution of MIMO ANNs. MIMO ANNs are important for problems that have multiple outputs. Examples are controllers for autonomous systems such as unmanned aerial vehicles (UAVs) and driver-less cars that take in multiple inputs and are expected to produce multiple outputs simultaneously such as speed, steering etc. Certain regression problems are also MIMO in nature. Our results are promising.