Cooperative co-evolution of GA-based classifiers based on input increments

Genetic algorithms (GAs) have been widely used as soft computing techniques in various applications, while cooperative co-evolution algorithms were proposed in the literature to improve the performance of basic GAs. In this paper, a new cooperative co-evolution algorithm, namely ECCGA, is proposed in the application domain of pattern classification. Concurrent local and global evolution and conclusive global evolution are proposed to improve further the classification performance. Different approaches of ECCGA are evaluated on benchmark classification data sets, and the results show that ECCGA can achieve better performance than the cooperative co-evolution genetic algorithm and normal GA. Some analysis and discussions on ECCGA and possible improvement are also presented

AN INVESTIGATION OF EVOLUTIONARY COMPUTING IN SYSTEMS IDENTIFICATION FOR PRELIMINARY DESIGN

This research investigates the integration of evolutionary techniques for symbolic regression. In particular the genetic programming paradigm is used together with other evolutionary computational techniques to develop novel approaches to the improvement of areas of simple preliminary design software using empirical data sets. It is shown that within this problem domain, conventional genetic programming suffers from several limitations, which are overcome by the introduction of an improved genetic programming strategy based on node complexity values, and utilising a steady state algorithm with subpopulations. A further extension to the new technique is introduced which incorporates a genetic algorithm to aid the search within continuous problem spaces, increasing the robustness of the new method. The work presented here represents an advance in the Geld of genetic programming for symbolic regression with significant improvements over the conventional genetic programming approach. Such improvement is illustrated by extensive experimentation utilising both simple test functions and real-world design examples

MAGPIE: Machine Automated General Performance Improvement via Evolution of Software

Performance is one of the most important qualities of software. Several techniques have thus been proposed to improve it, such as program transformations, optimisation of software parameters, or compiler flags. Many automated software improvement approaches use similar search strategies to explore the space of possible improvements, yet available tooling only focuses on one approach at a time. This makes comparisons and exploration of interactions of the various types of improvement impractical. We propose MAGPIE, a unified software improvement framework. It provides a common edit sequence based representation that isolates the search process from the specific improvement technique, enabling a much simplified synergistic workflow. We provide a case study using a basic local search to compare compiler optimisation, algorithm configuration, and genetic improvement. We chose running time as our efficiency measure and evaluated our approach on four real-world software, written in C, C++, and Java. Our results show that, used independently, all techniques find significant running time improvements: up to 25% for compiler optimisation, 97% for algorithm configuration, and 61% for evolving source code using genetic improvement. We also show that up to 10% further increase in performance can be obtained with partial combinations of the variants found by the different techniques. Furthermore, the common representation also enables simultaneous exploration of all techniques, providing a competitive alternative to using each technique individually.Comment: 19 page

System identification and pid control of toothbrush simulator system

Toothbrush simulator was invented for industry and dentist researchers to do research related to plaque removal. The toothbrush simulator system repeatedly has a problem in achieving the desired speed control. The brushing movement is inconsistence and stops eventually if there is a force exerted on the toothbrush holder. Further research is required to increase the reliability and controllability of the speed response achievable from the toothbrush simulator system. In this study, a PID controller is designed and embedded in the system. A real-time experiment has been conducted on the real system via the Matlab Simulink environment to construct the model. The model parameters are optimized with model order 2, 3 and 4 where each model order has been analyzed for ten (10) times iteration by the genetic algorithm in obtaining the accurate transfer function. The model has been validated through correlation analysis. The PID controller was tuned through the PID tuner and Ziegler-Nichols method. Simulated and real-time system response from both tuning methods was compared. The simulated response with the selected PID controller is then compared with the response from the real-time experiment. The closed-loop system without controller was compared with the response with the PID controller. The PID controller was then deployed into the real system by uploaded into the microcontroller. The brushing simulator remote control was created to control the desired speed through a smartphone. Genetic algorithm model based on model order 4 has been selected as the best model as it able to achieve the minimum MSE value of 0.0176 and past all the validation tests. The selected PID parameters was from PID tuner tuning method with gain values of; Kp= 17.9287, Ki= 40.751 and Kd= -0.52705. Both results of simulation and real-time tests were compared, and they show about similar performances. The controlled system response had achieved all five desired speed of 175, 195, 215, 235 and 255 rpm with the percentage of improvement 67%, 65%, 65%, 65%, and 68%. Throughout this study, a genetic algorithm model based and tuned PID controller parameters has been applied to the real system improvised in better system response

Use of q-values to Improve a Genetic Algorithm to Identify Robust Gene Signatures

Several approaches have been proposed for the analysis of DNA microarray datasets, focusing on the performance and robustness of the final feature subsets. The novelty of this paper arises in the use of q-values to pre-filter the features of a DNA microarray dataset identifying the most significant ones and including this information into a genetic algorithm for further feature selection. This method is applied to a lung cancer microarray dataset resulting in similar performance rates and greater robustness in terms of selected features (on average a 36.21% of robustness improvement) when compared to results of the standard algorithm

GeneRank: Using search engine technology for the analysis of microarray experiments

Copyright @ 2005 Morrison et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Background: Interpretation of simple microarray experiments is usually based on the fold-change of gene expression between a reference and a "treated" sample where the treatment can be of many types from drug exposure to genetic variation. Interpretation of the results usually combines lists of differentially expressed genes with previous knowledge about their biological function. Here we evaluate a method – based on the PageRank algorithm employed by the popular search engine Google – that tries to automate some of this procedure to generate prioritized gene lists by exploiting biological background information. Results: GeneRank is an intuitive modification of PageRank that maintains many of its mathematical properties. It combines gene expression information with a network structure derived from gene annotations (gene ontologies) or expression profile correlations. Using both simulated and real data we find that the algorithm offers an improved ranking of genes compared to pure expression change rankings. Conclusion: Our modification of the PageRank algorithm provides an alternative method of evaluating microarray experimental results which combines prior knowledge about the underlying network. GeneRank offers an improvement compared to assessing the importance of a gene based on its experimentally observed fold-change alone and may be used as a basis for further analytical developments

Algorithmic simulation in system design and innovation

Thesis (S.M. in Engineering and Management)--Massachusetts Institute of Technology, Engineering Systems Division, System Design and Management Program, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 61-63).This thesis explores the use of genetic programming as a tool in the system design and innovation process. Digital circuits are used as a proxy for complex technological designs. Circuit construction is simulated through a computer algorithm which assembles circuit designs in an attempt to reach specified design goals. Complex designs can be obtained by repeatedly combining simpler components, often called building blocks, which were created earlier in the algorithm's progression. This process is arguably a reflection of the traditional development path of systems engineering and technological innovation. The choice of algorithm used to guide this process is crucial. This thesis considers two general types of algorithms-a blind random search method, and a genetic programming search method-with variations applied to each. The research focused on comparing these algorithms in regard to: 1) the successful creation of multiple complex designs; 2) resources utilized in achieving a design of a given complexity; and 3) the inferred time dependence of technological improvement resulting from the process. Also of interest was whether these algorithms would exhibit exponential rates of improvement of the virtual technologies being created, as is seen in real-world innovation. The starting point was the hypothesis that the genetic programming approach might be superior to the random search method. The results found however that the genetic programming algorithm did not outperform the blind random search algorithm, and in fact failed to produce the desired circuit design goals. This unexpected outcome is believed to result from the structure of the circuit design process, and from certain shortcomings in the genetic programming algorithm used. This work also examines the relationship of issues and considerations (such as cost, complexity, performance, and efficiency) faced in these virtual design realms to managerial strategy and how insights from these experiments might be applied to real-world engineering and design challenges. Algorithmic simulation approaches, including genetic programming, are found to be powerful tools, having demonstrated impressive performance in bounded domains. However, their utility to systems engineering processes remains unproven. Therefore, use of these algorithmic tools and their integration into the human creative process is discussed as a challenge and an area needing further research.by Timothy Harsh.S.M.in Engineering and Managemen

Multi-point and multi-objective optimization of a centrifugal compressor impeller based on genetic algorithm

The design of high efficiency, high pressure ratio, and wide flow range centrifugal impellers is a challenging task. The paper describes the application of a multiobjective, multipoint optimization methodology to the redesign of a transonic compressor impeller for this purpose. The aerodynamic optimization method integrates an improved nondominated sorting genetic algorithm II (NSGA-II), blade geometry parameterization based on NURBS, a 3D RANS solver, a self-organization map (SOM) based data mining technique, and a time series based surge detection method. The optimization results indicate a considerable improvement to the total pressure ratio and isentropic efficiency of the compressor over the whole design speed line and by 5.3% and 1.9% at design point, respectively. Meanwhile, surge margin and choke mass flow increase by 6.8% and 1.4%, respectively. The mechanism behind the performance improvement is further extracted by combining the geometry changes with detailed flow analysis

Enhancement of speed and efficiency of an Internet based gear design optimisation

An internet-based gear design optimisation program has been developed for geographically dispersed teams to collaborate over the internet. The optimisation program implements genetic algorithm. A novel methodology is presented that improves the speed of execution of the optimisation program by integrating artificial neural networks into the system. The paper also proposes a method that allows an improvement to the performance of the back propagation-learning algorithm. This is done by rescaling the output data patterns to lie slightly below and above the two extreme values of the full range neural activation function. Experimental tests show the reduction of execution time by approximately 50%, as well as an improvement in the training and generalisation errors and the rate of learning of the network