Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
An introduction to fuzzy control
An introduction to fuzzy control
A learning process for fuzzy control rules using genetic algorithms
Fuzzy Sets and Systems
Fuzzy modeling with hybrid systems
Fuzzy Sets and Systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Three objective genetics-based machine learning for linguisitc rule extraction
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Fuzzy Control of HVAC Systems Optimized by Genetic Algorithms
Applied Intelligence
An Adaptive, Intelligent Control System for Slag Foaming
Applied Intelligence
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
Hybrid learning models to get the interpretability–accuracy trade-off in fuzzy modeling
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on soft computing for information mining
International Journal of Approximate Reasoning
A Pareto-based multi-objective evolutionary approach to the identification of Mamdani fuzzy systems
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Fuzzy classifier identification using decision tree and multiobjective evolutionary algorithms
International Journal of Approximate Reasoning
Developing a bioaerosol detector using hybrid genetic fuzzy systems
Engineering Applications of Artificial Intelligence
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Genetic Fuzzy Systems: Recent Developments and Future Directions; Guest editors: Jorge Casillas, Brian Carse
On the Effect of the Steady-State Selection Scheme in Multi-Objective Genetic Algorithms
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
Improving fuzzy logic controllers obtained by experts: a case study in HVAC systems
Applied Intelligence
Engineering Applications of Artificial Intelligence
IEEE Transactions on Fuzzy Systems
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
A 2-tuple fuzzy linguistic representation model for computing with words
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Selecting fuzzy if-then rules for classification problems using genetic algorithms
IEEE Transactions on Fuzzy Systems
International Journal of Approximate Reasoning
Interpretability of linguistic fuzzy rule-based systems: An overview of interpretability measures
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Demand-driven power saving by multiagent negotiation for HVAC control
Joint Proceedings of the Workshop on AI Problems and Approaches for Intelligent Environments and Workshop on Semantic Cities
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This paper focuses on the use of multi-objective evolutionary algorithms to develop smartly tuned fuzzy logic controllers dedicated to the control of heating, ventilating and air conditioning systems, energy performance, stability and indoor comfort requirements. This problem presents some specific restrictions that make it very particular and complex because of the large time requirements needed to consider multiple criteria (which enlarge the solution search space) and the long computation time models required in each evaluation.In this work, a specific multi-objective evolutionary algorithm is proposed to obtain more compact fuzzy logic controllers as a way of finding the best combination of rules, thus improving the system performance to better solve the HVAC system control problem. This method combines lateral tuning of the linguistic variables with rule selection. To this end, two objectives have been considered, maximizing the performance of the system and minimizing the number of rules obtained. This algorithm is based on the well-known SPEA2 but uses different mechanisms for guiding the search towards the desired Pareto zone. Moreover, the method implements some advanced concepts such as incest prevention, that help to improve the exploration/exploitation trade-off and consequently its convergence ability.The proposed method is compared to the most representative mono-objective steady-state genetic algorithms previously applied to the HVAC system control problem, and to generational and steady-state versions of the most interesting multi-objective evolutionary algorithms (never applied to this problem) showing that the solutions obtained by this new approach dominate those obtained by these methods. The results obtained confirm the effectiveness of our approach compared with the rest of the analyzed methods, obtaining more accurate fuzzy logic controllers with simpler models.