By mapping the solution spaces into heuristic space, it would be possible to make easy decision to settle data clustering problems. Our suggested hyperheuristic clustering works into three major spaces including high-level space, low-level space and problem space. The experiments of this study have...
Del Ser "A new grouping genetic algorithm for clustering problems" Expert Systems with Applications 39 (2012) 96959703. [3] Daniel Barbard, "Requirements for Clustering Data Streams, " ACM SIGKDD Explorations Newsletter, vol. 3, pp. 23-27, 2002. [4] Jurgen Beringer, Eyke Hullermer...
In this paper the performance of genetic algorithms for solving some clustering problems is investigated through a simulation experiment. If the number of clusters is known in advance, our results show that the genetic algorithm is able to find the right partition, almost irrespective of the ...
Improved particle swarm optimization algorithm According to the no free lunch theory31, it is known that no algorithm can solve every practical problem with high quality and efficiency for increasingly complex and diverse optimization problems. In this section, several improvement strategies are proposed...
The whale optimization algorithm has received much attention since its introduction due to its outstanding performance. However, like other algorithms, the whale optimization algorithm still suffers from some classical problems. To address the issues of
the complexities and challenges in the classification of multiclass and imbalanced fault conditions, this study explores the systematic combination of unsupervised and supervised learning by hybridising clustering (CLUST) and optimised multi-layer perceptron neural network with grey wolf algorithm (GWO-MLP...
(integer parameters). This method usually can't acceptably solve high-dimensional continuous problems that are implicitly combinatorial (e.g., Perm, and Lennard-Jones atom clustering problems) as in such problems the global descent vanishes at some point and the method is left with an ...
Propose a multi-parent genetic algorithm for solving longitude–latitude-based 4D traveling salesman problems under uncertainty. • Apply a memetic genetic algorithm with four parents crossover, probabilistic selection, and random mutation. • Test the model with various indexes from the traveling sal...
The first uses hierarchical clustering to partition the observed demonstrations into subtasks. The second uses IRL to estimate the reward function for each subtask. The third solves the forward problem and returns a policy. Kirshnan et al. present experiments that demonstrate that SWIRL requires ...
To show the genetic divergence among identified clusters, pairwise Fst81 was calculated using Arlequin3.582 at 10,000 permutations. Gaussian finite mixture model-based clustering of the collection was fitted via the EM algorithm in R package mclust83 using phenotypic data. We also visualized the ...