Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Machine Learning
Computational Statistics & Data Analysis - Nonlinear methods and data mining
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management
Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
On the Choice of the Offspring Population Size in Evolutionary Algorithms
Evolutionary Computation
Fuzzy Logic, Identification and Predictive Control (Advances in Industrial Control)
Fuzzy Logic, Identification and Predictive Control (Advances in Industrial Control)
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Introducing robustness in multi-objective optimization
Evolutionary Computation
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
A competitive-cooperative coevolutionary paradigm for dynamic multiobjective optimization
IEEE Transactions on Evolutionary Computation - Special issue on computational finance and economics
Multiobjective evolutionary algorithm for the optimization of noisy combustion processes
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A distributed evolutionary classifier for knowledge discovery in data mining
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Genetic algorithms with a robust solution searching scheme
IEEE Transactions on Evolutionary Computation
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
Dynamic multiobjective optimization problems: test cases, approximations, and applications
IEEE Transactions on Evolutionary Computation
Evolutionary multiobjective industrial design: the case of a racing car tire-suspension system
IEEE Transactions on Evolutionary Computation
Multiobjective evolutionary algorithms for electric power dispatch problem
IEEE Transactions on Evolutionary Computation
A Multiobjective Optimization-Based Evolutionary Algorithm for Constrained Optimization
IEEE Transactions on Evolutionary Computation
Combining Neural Networks and Genetic Algorithms to Predict and Reduce Diesel Engine Emissions
IEEE Transactions on Evolutionary Computation
An Investigation on Preference Order Ranking Scheme for Multiobjective Evolutionary Optimization
IEEE Transactions on Evolutionary Computation
An Investigation on Noisy Environments in Evolutionary Multiobjective Optimization
IEEE Transactions on Evolutionary Computation
A multiobjective evolutionary algorithm toolbox for computer-aidedmultiobjective optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Multiobjective Memetic Algorithm Based on Particle Swarm Optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Compact Particle Swarm Optimization
Information Sciences: an International Journal
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This paper presents a dynamic predictiveoptimization framework of a nonlinear temporal process. Datamining (DM) and evolutionary strategy algorithms are integrated in the framework for solving the optimization model. DM algorithms learn dynamic equations from the process data. An evolutionary strategy algorithm is then applied to solve the optimization problem guided by the knowledge extracted by the DM algorithm. The concept presented in this paper is illustrated with the data from a power plant, where the goal is to maximize the boiler efficiency and minimize the limestone consumption. This multiobjective optimization problem can be either transformed into a single-objective optimization problem through preference aggregation approaches or into a Pareto-optimal optimization problem. The computational results have shown the effectiveness of the proposed optimization framework.