Kisung Seo

Fuzzy Radial Basis Function Neural Networks with information granulation and its parallel genetic optimization

Fuzzy modeling of complex systems is a challenging task, which involves important problems of dimensionality reduction and calls for various ways of improving the accuracy of modeling. The IG-FRBFNN, a hybrid architecture of the IG-FIS (Fuzzy Inference System) and FRBFNN (Fuzzy Radial Basis Function Neural Networks), is proposed to address these problems. The paper is concerned with the analysis and design of IG-FRBFNNs and their optimization by means of the Hierarchical Fair Competition-based Parallel Genetic Algorithm (HFC-PGA). In the proposed network, the membership functions of the premise part of the fuzzy rules of the IG-based FRBFNN model directly rely on the computation of the relevant distance between data points and the use of four types of polynomials such as constant, linear, quadratic and modified quadratic are considered for the consequent part of fuzzy rules. Moreover, the weighted Least Square (WLS) learning is exploited to estimate the coefficients of the polynomial forming the conclusion part of the rules. Since the performance of the IG-RBFNN model is affected by some key design parameters, such as a specific subset of input variables, the fuzzification coefficient of the FCM, the number of rules, and the order of polynomial of the consequent part of fuzzy rules, it becomes beneficial to carry out both structural as well as parametric optimization of the network. In this study, the HFC-PGA is used as a comprehensive optimization vehicle. The performance of the proposed model is illustrated by means of several representative numerical examples.

Genetic Programming-Based Automatic Gait Generation in Joint Space for a Quadruped Robot

This paper introduces a new approach to developing a fast gait for a quadruped robot using genetic programming (GP). Planning gaits for legged robots is a challenging task that requires optimizing parameters in a highly irregular and multi-dimensional space. Several recent approaches have focused on using genetic algorithms (GAs) to generate gaits automatically and have shown significant improvement over previous gait optimization results. Most current GA-based approaches optimize only a small, pre-selected set of parameters, but it is difficult to decide which parameters should be included in the optimization to get the best results. Moreover, the number of pre-selected parameters is at least 10, so it can be relatively difficult to optimize them, given their high degree of interdependence. To overcome these problems of the typical GA-based approach, we have proposed a seemingly more efficient approach that optimizes joint trajectories instead of locus-related parameters in Cartesian space, using GP. Our GP-based method has obtained much-improved results over the GA-based approaches tested in experiments on the Sony AIBO ERS-7 in the Webots environment. The elite archive mechanism is introduced to combat the premature convergence problems in GP and has shown better results than a traditional multi-population approach.

Genetic programming based automatic gait generation for quadruped robots

This paper introduces a new approach to develop fast gait for quadruped robot using genetic programming (GP). Several recent approaches have focused on using genetic algorithm (GA) to generate gait automatically and shown significant improvements over previous results. Most of current GA based approaches use pre-selected parameters, but it is difficult to select the appropriate parameters for the optimization of gait. To overcome these problems of GA based approach, we proposed an efficient approach which optimizes joint angle trajectories using genetic programming. Our GP based method has obtained much better results than GA based approaches for experiments of Sony AIBO ers-7 in Webots environment. The elite archive mechanism(EAM) was introduced to prevent premature convergence problems in GP and has shown improvements.

The genetic algorithm based route finding method for alternative paths

Presents an effective alternative paths calculation method based on a genetic algorithm. We developed efficient genetic operators for path calculation. A major problem of the existing approach-similarities among the paths-can be resolved using GAs. The performance of the suggested technique is evaluated and compared with the k-th shortest path for the virtual road network model by computer simulation. The results of computational experiments of the suggested method are found to be satisfactory in terms of the spread of alternatives.

A spanning tree-based encoding of the MAX CUT problem for evolutionary search

Most of previous genetic algorithms for solving graph problems have used vertex-based encoding. In this paper, we introduce spanning tree-based encoding instead of vertex-based encoding for the well-known MAX CUT problem. We propose a new genetic algorithm based on this new type of encoding. We conducted experiments on benchmark graphs and could obtain performance improvement on sparse graphs, which appear in real-world applications such as social networks and systems biology, when the proposed methods are compared with ones using vertex-based encoding.

A comparative study between genetic algorithm and genetic programming based gait generation methods for quadruped robots

Planning gaits for legged robots is a challenging task that requires optimizing parameters in a highly irregular and multidimensional space. Two gait generation methods using GA (Genetic Algorithm), GP (genetic programming) are compared to develop fast locomotion for a quadruped robot. GA-based approaches seek to optimize a pre-selected set of parameters which include locus of paw and stance parameters of initial position. A GP-based technique is an effective way to generate a few joint trajectories instead of the locus of paw positions and many stance parameters. Optimizations for two proposed methods are executed and analyzed using a Webots simulation of the quadruped robot built by Bioloid. Furthermore, simulation results for the two proposed methods are tested in a real quadruped robot, and the performance and motion features of GA-, GP -based methods are compared.

Genetic Programming based Illumination Robust and Non-parametric Multi-colors Detection Model

This paper introduces GP(Genetic Programming) based color detection model for an object detection and tracking. Existing color detection methods have used linear/nonlinear transformatin of RGB color-model and improved color model for illumination variation by optimization or learning techniques. However, most of cases have difficulties to classify various of colors because of interference of among color channels and are not robust for illumination variation. To solve these problems, we propose illumination robust and non-parametric multi-colors detection model using evolution of GP. The proposed method is compared to the existing color-models for various colors and images with different lighting conditions.

Automatic Gait Generation for Quadruped Robot Using GA with an Enhancement of Performance

This Paper introduces new approach to develop fast and reliable gaits for quadruped robot using GA(genetic algorithm). Planning gaits for legged robots is a challenging task that requires optimizing parameters in a highly irregular and multidimensional space. Recent approaches have problems to select proper parameters which are not known in advance and optimize more than ten to twenty parameters simultaneously. In our approach, the effects of major gait parameters are analysed and used to guide the search more efficiently. The experiments of Sony AIBO ERS-7 in Webots environment indicate that our approach is able to produce much improved results in fast velocity and reliability.

Simultaneous Optimization of ANFIS-Based Fuzzy Model Driven to Data Granulation and Parallel Genetic Algorithms

The paper concerns the simultaneous optimization for structure and parameters of fuzzy inference systems that is based on Hierarchical Fair Competition-based Parallel Genetic Algorithms (HFCGA) and information data granulation. HFCGA is used to optimize structure and parameters of ANFIS-based fuzzy model simultaneously. The granulation is realized with the aid of the C-means clustering. Through the simultaneous optimization mechanism to be explored, we can find the overall optimal values related to structure as well as parameter identification of ANFIS-based fuzzy model via HFCGA, C-Means clustering and standard least square method. A comparative analysis demon-strates that the proposed algorithm is superior to the conventional methods.

Genetic programming-based model output statistics for short-range temperature prediction

This paper introduces GP (Genetic Programming) based robust compensation technique for temperature prediction in short-range. MOS (Model Output Statistics) is a statistical technique that corrects the systematic errors of the model. Development of an efficient MOS is very important, but most of MOS are based on the idea of relating model forecasts to observations through a linear regression. Therefore it is hard to manage complex and irregular natures of the prediction. In order to solve the problem, a nonlinear and symbolic regression method using GP is suggested as the first attempt. The purpose of this study is to evaluate the accuracy of the estimation by GP based nonlinear MOS for the 3 days temperatures for Korean regions. This method is then compared to the UM model and shows superior results. The training period of summer in 2007-2009 is used, and the data of 2010 summer is adopted for verification.