Soft computing

About: Soft computing is a research topic. Over the lifetime, 6710 publications have been published within this topic receiving 118508 citations.

Genetic-Algorithm-Based Strategies for Dynamic Identification of Nonlinear Systems with Noise-Corrupted Response

Abstract: The main objective of this paper is to investigate efficiency and correctness of different real-coded genetic algorithms and identification criteria in nonlinear system identification within the framework of non-classical identification techniques. Two conventional genetic algorithms have been used, standard genetic algorithm and microgenetic algorithm. Moreover, an advanced multispecies genetic algorithm has been proposed: it combines an adaptive rebirth operator, a migration strategy, and a search space reduction technique. Initially, a critical analysis has been conducted on these soft computing strategies to provide some guidelines for similar engineering and physical applications. Therefore, the hysteretic Bouc-Wen model has been numerically investigated to achieve three main results. First, the computational effectiveness and accuracy of the proposed strategy are checked to show that the proposed optimizer outperforms the aforementioned conventional genetic algorithms. Secondarily, a comparative study is performed to show that an improved performance can be obtained by using the Hilbert transform-based acceleration envelope as objective function in the optimization problem (instead of the pure acceleration response). Finally, system identification is conducted by making use of the proposed optimizer to verify its substantial noise-insensitive property also in the presence of high noise-to-signal ratio.

Soft Computing Techniques Applied to Combinatorial Catalysis: A New Approach for the Discovery and Optimization of Catalytic Materials

Abstract: A soft computing technique based on the combination of Artificial Neural Networks (ANNs) and a Genetic Algorithm (GA) has been developed for the discovery and optimization of new materials when exploring a high-dimensional space. This technique allows the experimental design in the search of new solid materials with high catalytic performance when exploring simultaneously a large number of variables such as elemental composition, manufacture procedure variables, etc. This novel integrated architecture allows one to strongly increase the convergence performance when compared with the performance of conventional GAs. It is described how both artificial intelligence techniques are built to work together. Moreover, the influence of algorithm configuration and the different algorithm parameters in the final optimization performance have been evaluated. The proposed optimization architecture has been validated using two hypothetical functions, based on the modeled behavior of multi-component catalysts explored in the field of combinatorial catalysis.

Enhancing Use Case Points Estimation Method Using Soft Computing Techniques

Abstract: Software estimation is a crucial task in software engineering. Software estimation encompasses cost, effort, schedule, and size. The importance of software estimation becomes critical in the early stages of the software life cycle when the details of software have not been revealed yet. Several commercial and non-commercial tools exist to estimate software in the early stages. Most software effort estimation methods require software size as one of the important metric inputs and consequently, software size estimation in the early stages becomes essential. One of the approaches that has been used for about two decades in the early size and effort estimation is called use case points. Use case points method relies on the use case diagram to estimate the size and effort of software projects. Although the use case points method has been widely used, it has some limitations that might adversely affect the accuracy of estimation. This paper presents some techniques using fuzzy logic and neural networks to improve the accuracy of the use case points method. Results showed that an improvement up to 22% can be obtained using the proposed approach.

Innovations in Hybrid Intelligent Systems

Abstract: Hybrid Artificial Intelligence Systems.- Hybrid Artificial Intelligence Systems.- Agents and Multiagent Systems.- Analysis of Emergent Properties in a Hybrid Bio-inspired Architecture for Cognitive Agents.- Using Semantic Causality Graphs to Validate MAS Models.- A Multiagent Framework to Animate Socially Intelligent Agents.- Context Aware Hybrid Agents on Automated Dynamic Environments.- Sensitive Stigmergic Agent Systems - A Hybrid Approach to Combinatorial Optimization.- Fuzzy Systems.- Agent-Based Social Modeling and Simulation with Fuzzy Sets.- Stage-Dependent Fuzzy-valued Loss Function in Two-Stage Binary Classifier.- A Feature Selection Method Using a Fuzzy Mutual Information Measure.- Interval Type-2 ANFIS.- A Vision-Based Hybrid Classifier for Weeds Detection in Precision Agriculture Through the Bayesian and Fuzzy k-Means Paradigms.- Artificial Neural Networks.- Development of Multi-output Neural Networks for Data Integration - A Case Study.- Combined Projection and Kernel Basis Functions for Classification in Evolutionary Neural Networks.- Modeling Heterogeneous Data Sets with Neural Networks.- A Computational Model of the Equivalence Class Formation Psychological Phenomenon.- Data Security Analysis Using Unsupervised Learning and Explanations.- Finding Optimal Model Parameters by Discrete Grid Search.- Clustering and Multiclassfier Systems.- A Hybrid Algorithm for Solving Clustering Problems.- Clustering Search Heuristic for the Capacitated p-Median Problem.- Experiments with Trained and Untrained Fusers.- Fusion of Visualization Induced SOM.- Robots.- Open Intelligent Robot Controller Based on Field-Bus and RTOS.- Evolutionary Controllers for Snake Robots Basic Movements.- Evolution of Neuro-controllers for Multi-link Robots.- Solving Linear Difference Equations by Means of Cellular Automata.- Metaheuristics and Optimization Models.- Automated Classification Tree Evolution Through Hybrid Metaheuristics.- Machine Learning to Analyze Migration Parameters in Parallel Genetic Algorithms.- Collaborative Evolutionary Swarm Optimization with a Gauss Chaotic Sequence Generator.- A New PSO Algorithm with Crossover Operator for Global Optimization Problems.- Solving Bin Packing Problem with a Hybridization of Hard Computing and Soft Computing.- Design of Artificial Neural Networks Based on Genetic Algorithms to Forecast Time Series.- Experimental Analysis for the Lennard-Jones Problem Solution.- Application of Genetic Algorithms to Strip Hot Rolling Scheduling.- Synergy of PSO and Bacterial Foraging Optimization - A Comparative Study on Numerical Benchmarks.- Artificial Vision.- Bayes-Based Relevance Feedback Method for CBIR.- A Novel Hierarchical Block Image Retrieval Scheme Based Invariant Features.- A New Unsupervised Hybrid Classifier for Natural Textures in Images.- Visual Texture Characterization of Recycled Paper Quality.- Case-Based Reasoning.- Combining Improved FYDPS Neural Networks and Case-Based Planning - A Case Study.- CBR Contributions to Argumentation in MAS.- Case-Base Maintenance in an Associative Memory Organized by a Self-Organization Map.- Hybrid Multi Agent-Neural Network Intrusion Detection with Mobile Visualization.- Learning Models.- Knowledge Extraction from Environmental Data Through a Cognitive Architecture.- A Model of Affective Entities for Effective Learning Environments.- Bioinformatics.- Image Restoration in Electron Cryotomography - Towards Cellular Ultrastructure at Molecular Level.- SeqTrim - A Validation and Trimming Tool for All Purpose Sequence Reads.- A Web Tool to Discover Full-Length Sequences - Full-Lengther.- Discovering the Intrinsic Dimensionality of BLOSUM Substitution Matrices Using Evolutionary MDS.- Autonomous FYDPS Neural Network-Based Planner Agent for Health Care in Geriatric Residences.- Structure-Preserving Noise Reduction in Biological Imaging.- Ensemble of Support Vector Machines to Improve the Cancer Class Prediction Based on the Gene Expression Profiles.- NATPRO-C13 - An Interactive Tool for the Structural Elucidation of Natural Compounds.- Application of Chemoinformatic Tools for the Analysis of Virtual Screening Studies of Tubulin Inhibitors.- Identification of Glaucoma Stages with Artificial Neural Networks Using Retinal Nerve Fibre Layer Analysis and Visual Field Parameters.- Dimensional Reduction in the Protein Secondary Structure Prediction - Nonlinear Method Improvements.- Other Applications.- Focused Crawling for Retrieving Chemical Information.- Optimal Portfolio Selection with Threshold in Stochastic Market.- Classification Based on Association Rules for Adaptive Web Systems.- Statistical Selection of Relevant Features to Classify Random, Scale Free and Exponential Networks.- Open Partner Grid Service Architecture in eBusiness.- An Architecture to Support Programming Algorithm Learning by Problem Solving.- Explain a Weblog Community.- Implementing Data Mining to Improve a Game Board Based on Cultural Algorithms.