An analysis on separability for Memetic Computing automatic design

2015 IEEE Symposium Series on Computational Intelligence

2015

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Abstract-Exact methods for Agglomerative Hierarchical Clustering (AHC) with average linkage do not scale well when the number of items to be clustered is large. The best known algorithms are characterized by quadratic complexity. This is a generally accepted fact and cannot be improved without using specifics of certain metric spaces. Twister tries is an algorithm that produces a dendrogram (i.e, outcome of a hierarchical clustering) which resembles the one produced by AHC, while only needing linear space and time. However, twister tries are sensitive to rare, but still possible, hash evaluations. These might have a disastrous effect on the final outcome. We propose the use of a metaheuristic algorithm to overcome this sensitivity and show how approximate computations of dendrogram quality can help to evaluate the heuristic within reasonable time. The proposed metaheuristic is based on an evolutionary framework and integrates a surrogate model of the fitness within it to enhance the algorithmic performance in terms of computational time.

Section: A Metaheuristic Approach: a Tailored Evolutionary Algorithmmentioning

confidence: 99%

Scalable Hierarchical Clustering: Twister Tries with a Posteriori Trie Elimination

Cochez

2015 IEEE Symposium Series on Computational Intelligence

2015

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“…In this paper, the initial local search is performed by the so-called Short Distance Exploration or simply S algorithm, see [30], [31], and [32]. The S algorithm is a simple greedy local search that performs moves along the axes and halves its radius when it is unable to detect a better solution.…”

Section: B Super-fit Dementioning

confidence: 99%

Robust impaired speech segmentation using neural network mixture model

Iliya

Menzies²,

2014 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)

et al. 2014

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Abstract-This paper presents a signal processing technique for segmenting short speech utterances into unvoiced and voiced sections and identifying points where the spectrum becomes steady. The segmentation process is part of a system for deriving musculoskeletal articulation data from disordered utterances, in order to provide training feedback. The functioning of the signal processing technique has been optimized by selecting the parameters of the model. The optimization has been carried out by testing and comparing multiple Differential Evolution implementations, including a standard one, a memetic one, and a controlled randomized one. Numerical results have also been compared with a famous and efficient swarm intelligence algorithm. For the given problem, Differential Evolution schemes appear to display a very good performance as they can quickly reach a high quality solution. The binomial crossover appears, for the given problem, beneficial with respect to the exponential one. The controlled randomization appears to be the best choice in this case. The overall optimized system proved to segment well the speech utterances and efficiently detect its uninteresting parts.

“…see [25] and [12]. In multiobjective optimisation, the most critical operation is the selection since the fitness based comparisons must take into account the fact that a solution can be better performing than another in terms of one objective and not another.…”

Section: Introductionmentioning

confidence: 99%

A fast hypervolume driven selection mechanism for many-objective optimisation problems

Rostami

Swarm and Evolutionary Computation

2017

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Solutions to real-world problems often require the simultaneous optimisation of multiple conflicting objectives. In the presence of four or more objectives, the problem is referred to as a "many-objective optimisation problem". A problem of this category introduces many challenges, one of which is the e↵ective and e cient selection of optimal solutions.The hypervolume indicator (or s-metric), i.e. the size of dominated objective space, is an e↵ective selection criterion for many-objective optimisation. The indicator is used to measure the quality of a nondominated set, and can be used to sort solutions for selection as part of the contributing hypervolume indicator. However, hypervolume based selection methods can have a very high, if not infeasible, computational cost.The present study proposes a novel hypervolume driven selection mechanism for many-objective problems, whilst maintaining a feasible computational cost. This approach, named the Hypervolume Adaptive Grid Algorithm (HAGA), uses two-phases (narrow and broad) to prevent population-wide calculation of the contributing hypervolume indicator. Instead, HAGA only calculates the contributing hypervolume indicator for grid populations, i.e. for a few solutions, which are close in proximity (in the objective space) to a candidate solution when in competition for survival. The result is a trade-o↵ between complete accuracy in selecting the fittest individuals in regards to hypervolume quality, and a feasible computational time in many-objective space. The real-world e ciency of the proposed selection mechanism is demonstrated within the optimisation of a classifier for concealed weapon detection.