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Multi-relational algorithm for mining association rules in large databases
(2011-12-01)
Multi-relational data mining enables pattern mining from multiple tables. The existing multi-relational mining association rules algorithms are not able to process large volumes of data, because the amount of memory required ...
General subpopulation framework and taming the conflict inside populations
(MIT PressCambridge, Mass, 2015)
Structured evolutionary algorithms have been investigated for some time. However,
they have been under explored especially in the field of multi-objective optimization.
Despite good results, the use of complex dynamics ...
A Diversity-Adaptive Hybrid Evolutionary Algorithm to Solve a Project Scheduling Problem
(Springer, 2014-09)
In this paper, we address a project scheduling problem. This problem considers a priority optimization objective for project managers. This objective implies assigning the most effective set of human resources to each ...
A multi-objective adaptive immune algorithm for multi-application NoC mapping
(SPRINGERDORDRECHT, 2012)
Current SoC design trends are characterized by the integration of larger amount of IPs targeting a wide range of application fields. Such multi-application systems are constrained by a set of requirements. In such scenario ...
Essence-Based Clustering: A multi-strategic and highly-customizable clustering approach
(Institute of Electrical and Electronics Engineers Inc., 2017)
The choice of a good clustering algorithm is vital in many tasks to optimize results. Nowadays, the most used algorithms use only one strategy to find and form the clusters of data, which can limit the effectiveness of the ...
Multiarea optimal power flow using multiobjective evolutionary algorithm
(2009-12-17)
In this work the multiarea optimal power flow (OPF) problem is decoupled into areas creating a set of regional OPF subproblems. The objective is to solve the optimal dispatch of active and reactive power for a determined ...
Evolving decision trees with beam search-based initialization and lexicographic multi-objective evaluation
(ElsevierNew York, 2014-02-10)
Decision tree induction algorithms represent one of the most popular techniques for dealing with classification problems. However, traditional decision-tree induction algorithms implement a greedy approach for node splitting ...
Heuristically accelerated reinforcement learning modularization for multi-agent multi-objective problems
(2014)
This article presents two new algorithms for finding the optimal solution of a Multi-agent Multi-objective Reinforcement Learning problem. Both algorithms make use of the concepts of modularization and acceleration by a ...
Solving biobjective set covering problem using binary cat swarm optimization algorithm
(Springer Verlag, 2016)
A Hybrid Heuristic Approach to Solve the Multi Level Capacitated Lot Sizing Problem
(Ieee, 2011-01-01)
This paper presents preliminary results found by a hybrid heuristic applied to solve the Multi-Level Capacitated Lot Sizing Problem (MLCLSP). The proposed method combines a multi-population genetic algorithm and fix-and-optimize ...