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Selection and Pruning Algorithms for Bitmap Index Selection Problem Using Data Mining
TLDR
This work proposes an approach which first prunes the search space of this problem using data mining techniques, and then based on the new search space, it uses a greedy algorithm to select BJIs that minimize the cost of executing a set of queries and satisfy a storage constraint. Expand
Cooperative Bees Swarm for Solving the Maximum Weighted Satisfiability Problem
TLDR
This paper introduces a new intelligent approach or meta-heuristic named “Bees Swarm Optimization”, BSO for short, which is inspired from the behaviour of real bees and shows that BSO outperforms the other evolutionary algorithms especially AC-SAT, an ant colony algorithm for SAT. Expand
Bees swarm optimisation using multiple strategies for association rule mining
TLDR
A new ARM algorithm based on an improved version of bees swarm optimisation with three different heuristics for exploring the search area is proposed, which outperforms some other existing algorithms both in terms of fitness criterion and CPU time. Expand
A Data Mining Approach for selecting Bitmap Join Indices
TLDR
A data mining driven approach to prune the search space of bitmap join index selection problem uses not only frequencies, but also other parameters such as the size of dimension tables involved in the indexing process, size of each dimension tuple, and page size on disk. Expand
Multi-population Cooperative Bat Algorithm for Association Rule Mining
TLDR
This paper investigates multi-population cooperative version of bat algorithm for association rule mining (BAT-ARM) named MPB-ARM which is based on bat inspired algorithm and introduces a cooperative master-slave strategy between the subpopulations. Expand
Bees Swarm Optimization for Web Association Rule Mining
TLDR
Experimental results show that concerning both the fitness criterion and the CPU time, IARMGA algorithm improved AGA and ARMGA two other versions based on genetic algorithm already proposed in the literature. Expand
A memetic algorithm for the optimal winner determination problem
TLDR
This paper investigates a new selection strategy based on both fitness and diversity to choose individuals to participate in the reproduction phase of the memetic algorithm and enhances the algorithm by using a stochastic local search component combined with a specific crossover operator. Expand
An efficient multiple classifier system for Arabic handwritten words recognition
TLDR
An efficient multiple classifier system for Arabic handwritten words recognition by using Chebyshev moments enhanced with some Statistical and Contour-based Features for describing word images and combining several classifiers integrated at the decision level is proposed. Expand
Local Search Methods for the Optimal Winner Determination Problem in Combinatorial Auctions
TLDR
Both stochastic local search (SLS) and tabu search (TS) are studied for the optimal winner determination problem (WDP) in combinatorial auctions and show that the SLS provides competitive results and finds solutions of a higher quality than TS and Casanova methods. Expand
Multi-swarm bat algorithm for association rule mining using multiple cooperative strategies
TLDR
A cooperative multi-swarm bat algorithm based on the bat-inspired algorithm adapted to rule discovering problem (BAT-ARM) based on a new topology called Hybrid that merges Ring strategy with Master-slave plan previously developed in earlier work is proposed. Expand
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