CONTENTS -- PREFACE -- Chapter 1THEORETICAL FOUNDATIONS OF KNOWLEDGE MINING AND INTELLIGENT AGENT -- 1.1. Knowledge and Agent -- 1.2. Knowledge Mining from Databases -- 1.2.1. KMD tasks -- 1.2.1.1. Mining Association Rules -- 1.2.1.2. Classification -- 1.2.1.3. Clustering -- 1.2.1.4. Dependency Modeling -- 1.2.1.5. Change and Deviation Detection -- 1.2.1.6. Regression -- 1.2.1.7. Summarization -- 1.2.1.8. Causation Modeling -- 1.3. Intelligent Agents -- 1.3.1. Evolutionary computing -- 1.3.2. Swarm intelligence -- 1.3.2.1. Particle Swarm Optimization -- 1.3.2.2. Ant Colony Optimization (ACO) -- 1.3.2.3. Artificial Bee Colony (ABC) -- 1.3.2.4. Artificial Wasp Colony (AWC) -- 1.3.2.5. Artificial Termite Colony (ATC) -- 1.4. Summary -- References -- Chapter 2 THE USE OF EVOLUTIONARY COMPUTATION IN KNOWLEDGE DISCOVERY: THE EXAMPLE OF INTRUSION DETECTION SYSTEMS -- 2.1. Introduction -- 2.2. Background -- 2.2.1. Knowledge discovery and data mining -- 2.2.2. Evolutionary computation -- 2.2.3. Intrusion detection systems -- 2.3. The Role of Evolutionary Computation in KDD -- 2.3.1. Feature selection -- 2.3.2. Classification -- 2.3.2.1. Representation -- 2.3.2.2. Learning approaches -- 2.3.2.3. Rule discovery -- 2.3.3. Regression -- 2.3.4. Clustering -- 2.3.5. Comparison between classification and regression -- 2.4. Evolutionary Operators and Niching -- 2.4.1. Evolutionary operators -- 2.4.2. Niching -- 2.5. Fitness Function -- 2.6. Conclusions and Future Directions -- Acknowledgment -- References -- Chapter 3 EVOLUTION OF NEURAL NETWORK AND POLYNOMIAL NETWORK -- 3.1. Introduction -- 3.2. Evolving Neural Network -- 3.2.1. The evolution of connection weights -- 3.2.2. The evolution of architecture -- 3.2.3. The evolution of node transfer function -- 3.2.4. Evolution of learning rules -- 3.2.5. Evolution of algorithmic parameters.

3.3. Evolving Neural Network using Swarm Intelligence -- 3.3.1. Particle swarm optimization -- 3.3.2. Swarm intelligence for evolution of neural network architecture -- 3.3.2.1. Particle representation -- 3.3.2.2. Fitness evaluation -- 3.3.3. Simulation and results -- 3.4. Evolving Polynomial Network (EPN) using Swarm Intelligence -- 3.4.1. GMDH-type polynomial neural network model -- 3.4.2. Evolving polynomial network (EPN) using PSO -- 3.4.3. Parameters of evolving polynomial network (EPN) -- 3.4.3.1. Highest degree of the polynomials -- 3.4.3.2. Number of terms in the polynomials -- 3.4.3.3. Maximum unique features in each term of the polynomials -- 3.4.4. Experimental studies for EPN -- 3.5. Summary and Conclusions -- References -- Chapter 4 DESIGN OF ALLOY STEELS USING MULTI-OBJECTIVE OPTIMIZATION -- 4.1. Introduction -- 4.2. The Alloy Optimal Design Problem -- 4.3. Neurofuzzy Modeling for Mechanical Property Prediction -- 4.3.1. General scheme of neurofuzzy models -- 4.3.2. Incorporating knowledge into neurofuzzy models -- 4.3.3. Property prediction of alloy steels using neurofuzzy models -- 4.3.3.1. Tensile strength prediction for heat-treated alloy steels -- 4.3.3.2. Impact toughness prediction for heat-treated alloy steels -- 4.4. Introduction to Multi-Objective Optimization -- 4.5. Particle Swarm Algorithm for Multi-Objective Optimization -- 4.5.1. Particle swarm optimization algorithm -- 4.5.2. Adaptive evolutionary particle swarm optimization (AEPSO) algorithm -- 4.5.3. Comparing AEPSO with some leading multi-objective optimization algorithms -- 4.6. Multi-Objective Optimal Alloy Design Using AEPSO -- 4.6.1. Impact toughness oriented optimal design -- 4.6.2. Optimal alloy design with both tensile strength and impact toughness -- 4.7. Conclusions -- Acknowledgments -- References.

Chapter 5 AN EXTENDED BAYESIAN/HAPSO INTELLIGENT METHOD IN INTRUSION DETECTION SYSTEM -- 5.1. Introduction -- 5.2. Related Research -- 5.3. Preliminaries -- 5.3.1. Naive Bayesian classifier -- 5.3.2. Intrusion detection system -- 5.3.2.1. Architecture of IDS -- 5.3.2.2. Efficiency of IDS -- 5.3.2.3. Effectiveness -- 5.3.2.4. Performance of IDS -- 5.3.3. Feature selection -- 5.3.4. Particle swarm optimization -- 5.4. HAPSO for Learnable Bayesian Classifier in IDS -- 5.4.1. Adaptive PSO -- 5.4.2. Hybrid APSO -- 5.4.3. Learnable Bayesian classifier in IDS -- 5.5. Experiments -- 5.5.1. Description of intrusion data -- 5.5.1.1. Probing -- 5.5.1.2. Denial of service attacks -- 5.5.1.3. User to root attacks -- 5.5.1.4. Remote to user attacks -- 5.5.2. System parameters -- 5.5.3. Results -- 5.6. Conclusions and Future Research Directions -- References -- Chapter 6 MINING KNOWLEDGE FROM NETWORK INTRUSION DATA USING DATA MINING TECHNIQUES -- 6.1. Introduction -- 6.2. Mining Knowledge Using Data Mining Techniques -- 6.3. Association Rule Mining -- 6.4. Measuring Interestingness -- 6.5. Classification -- 6.6. Ensemble of Classifier -- 6.7. Clustering -- Types of Clustering Algorithms: -- Algorithm description: -- EM (Expectation Maximization) Clustering -- 6.8. Conclusion -- References -- Chapter 7 PARTICLE SWARM OPTIMIZATION FOR MULTI-OBJECTIVE OPTIMAL OPERATIONAL PLANNING OF ENERGY PLANTS -- 7.1. Introduction -- 7.2. Problem Formulation -- 7.2.1. State variables -- 7.2.2. Objective function -- 7.2.3. Constraints -- 7.3. Particle Swarm Optimization -- 7.3.1. Original PSO -- 7.3.2. Evolutionary PSO EPSO -- 7.3.3. Adaptive PSO(APSO) -- 7.3.4. Simple expansion of PSO for optimal operational planning -- 7.4. Optimal Operational Planning for Energy Plants Using PSO -- 7.5. Numerical Examples -- 7.5.1. Simulation conditions -- 7.5.2. Simulation results.

7.6. FeTOP - Energy Management System -- 7.7. Conclusions -- References -- Chapter 8 SOFT COMPUTING FOR FEATURE SELECTION -- 8.1. Introduction -- 8.1.1. Definition -- 8.2. Non-Soft Computing Techniques for Feature Selection -- 8.2.1. Enumerative algorithms -- 8.2.2. Sequential search algorithms -- 8.2.3. Sampling -- 8.2.4. Feature selection based on information theory -- 8.2.5. Floating search for feature selection -- 8.2.6. Feature selection for SVM -- Working Principle: -- 8.2.7. Feature weighting method -- 8.2.8. Feature selection with dynamic mutual information -- 8.2.9. Learning to classify by ongoing feature selection -- 8.2.10. Multiclass MTS for simultaneous feature selection and classification -- 8.3. Soft computing for feature selection -- 8.3.1. Genetic algorithm for feature selection -- 8.3.2. ELSA -- 8.3.3. Neural network for feature selection -- 8.4. Hybrid Algorithm for Feature Selection -- 8.4.1. Neuro-Fuzzy feature selection -- 8.5. Multi-Objective Genetic Algorithm for Feature Selection -- 8.6. Parallel Genetic Algorithm for Feature Selection -- Self-adaptive genetic algorithm for clustering (SAGA). -- Gene bank process -- 8.7. Unsupervised Techniques for Feature Selection -- 8.8. Evaluation functions -- 8.9. Summary and Conclusions -- References -- Chapter 9 OPTIMIZED POLYNOMIAL FUZZY SWARM NET FOR CLASSIFICATION -- 9.1. Introduction -- 9.2. Fuzzy Net Architecture -- 9.3. Particle Swarm Optimization -- 9.3.1. Fully informed particle swarm (FIPS) -- 9.3.2. Binary particle swarms -- 9.3.3. Hybrids and adaptive particle swarms -- 9.3.4. PSOs with diversity control -- 9.3.5. Bare-bones PSO -- 9.4. Fuzzy Swarm Net Classifier -- Pseudocode -- 9.5. Polynomial Neural Network -- 9.6. Classification with Optimized Polynomial Neural Fuzzy Swarm Net -- Pseudocode -- 9.7. Experimental Studies -- 9.7.1. Description of the datasets.

9.8. Conclusion -- References -- Chapter 10 SOFTWARE TESTING USING GENETIC ALGORITHMS -- 10.1. Introduction -- 10.2. Overview of Test Case Design -- 10.2.1. Path wise test data generators -- 10.3. Genetic Algorithm -- 10.3.1. Introduction to genetic algorithms -- 10.3.2. Overview of genetic algorithms -- 10.4. Path Wise Test Data Generation Based on GA -- 10.5. Summary -- References.