Classifier systems and genetic algorithms
Artificial Intelligence
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
A learning system based on genetic adaptive algorithms
A learning system based on genetic adaptive algorithms
The Grid 2: Blueprint for a New Computing Infrastructure
The Grid 2: Blueprint for a New Computing Infrastructure
A grid service broker for scheduling distributed data-oriented applications on global grids
MGC '04 Proceedings of the 2nd workshop on Middleware for grid computing
Distributed computing in practice: the Condor experience: Research Articles
Concurrency and Computation: Practice & Experience - Grid Performance
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on soft computing for information mining
Development of scheduling strategies with Genetic Fuzzy systems
Applied Soft Computing
Particle swarm optimization for biomass-fuelled systems with technical constraints
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Evolutionary Fuzzy Scheduler for Grid Computing
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Fuzzy Particle Swarm Optimization Algorithm
JCAI '09 Proceedings of the 2009 International Joint Conference on Artificial Intelligence
Engineering Applications of Artificial Intelligence
Using particle swam optimization for QoS in ad-hoc multicast
Engineering Applications of Artificial Intelligence
Euclidean Particle Swarm Optimization
ICINIS '09 Proceedings of the 2009 Second International Conference on Intelligent Networks and Intelligent Systems
Design of Mamdani fuzzy logic controllers with rule base minimisation using genetic algorithm
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence
An improved GA and a novel PSO-GA-based hybrid algorithm
Information Processing Letters
Adaptive particle swarm optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Fuzzy Systems
Alea: grid scheduling simulation environment
PPAM'07 Proceedings of the 7th international conference on Parallel processing and applied mathematics
Scheduling jobs on computational grids using a fuzzy particle swarm optimization algorithm
Future Generation Computer Systems
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Perception-based granular probabilities in risk modeling and decision making
IEEE Transactions on Fuzzy Systems
Design for Self-Organizing Fuzzy Neural Networks Based on Genetic Algorithms
IEEE Transactions on Fuzzy Systems
Incremental Evolutionary Design of TSK Fuzzy Controllers
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Parameterized uncertain reasoning approach based on a lattice-valued logic
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
International Journal of Approximate Reasoning
Fuzzy scheduling with swarm intelligence-based knowledge acquisition for grid computing
Engineering Applications of Artificial Intelligence
Knowledge discovery for scheduling in computational grids
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Evolutionary prediction of photovoltaic power plant energy production
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
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Knowledge acquisition is a long-standing problem in fuzzy-rule-based systems. In spite of the existence of several approaches, much effort is still required to increase the efficiency of the learning process. This study introduces a new method for the fuzzy-rule evolution that forms an expert system knowledge: the knowledge acquisition with a swarm-intelligence approach (KASIA). Specifically, this strategy is based on the use of particle-swarm optimization (PSO) to obtain the antecedents, consequences, and connectives of the rules. To test the feasibility of the suggested method, the inverted-pendulum problem is studied, and results are compared for two of the most extensively used methodologies in machine learning: the genetic-based Pittsburgh approach and the Q-learning-based strategy, i.e., state-action-reward-state-action (SARSA). Moreover, KASIA is analyzed as a learning strategy in fuzzy-rule-based metascheduler design for grid computing, and performance is compared with other scheduling strategies based on genetic learning and existing scheduling approaches, i.e., EASY-backfilling and ESG+local periodical search. To be more precise, simulation results prove the fact that the proposed strategy outperforms classical learning approaches in terms of final results and computational effort. Furthermore, the main advantage is the capability to control convergence and its simplicity.