A MOS-based Dynamic Memetic Differential Evolution Algorithm for Continuous Optimization: A Scalability Test

Continuous optimization is one of the areas with more activity in the field of heuristic optimization. Many algorithms have been proposed and compared on several benchmarks of functions, with different performance depending on the problems. For this reason, the combination of different search strategies seems desirable to obtain the best performance of each of these approaches. This contribution explores the use of a hybrid memetic algorithm based on the multiple offspring framework. The proposed algorithm combines the explorative/exploitative strength of two heuristic search methods that separately obtain very competitive results. This algorithm has been tested with the benchmark problems and conditions defined for the special issue of the Soft Computing Journal on Scalability of Evolutionary Algorithms and other Metaheuristics for Large Scale Continuous Optimization Problems. The proposed algorithm obtained the best results compared with both its composing algorithms and a set of reference algorithms that were proposed for the special issue

Protein structure prediction with a new composite measure of diversity and memory-based diversification strategy

Protein structure prediction (PSP) problem is a multimodal problem that can be tackled efficiently by evolutionary algorithms. However, evolutionary algorithms often fail to find the global optima due to genetic drift while solving the complex problems with a lot of peaks in the fitness landscape. Therefore, the need to efficiently measure as well as maintaining population diversity has significant effects in performance of evolutionary algorithms. In this paper, we introduce a composite measure of population diversity by hybridizing the phenotypic properties along with the distribution of individuals in a population over the fitness landscape. We further propose a memory-based diversification technique for the maintenance and promotion of diversity to prevent occurrence of stuck condition in multimodal problems such as PSP. Experiments conducted on protein structure prediction with HP benchmark sequences for 3D cubic lattice model illustrate that the proposed techniques are useful in improving the optimization process in terms of convergence as well as for achieving the optimal energ

Benchmark database for fine-grained image classification of benthic macroinvertebrates

Managing the water quality of freshwaters is a crucial task worldwide. One of the most used methods to biomonitor water quality is to sample benthic macroinvertebrate communities, in particular to examine the presence and proportion of certain species. This paper presents a benchmark database for automatic visual classification methods to evaluate their ability for distinguishing visually similar categories of aquatic macroinvertebrate taxa. We make publicly available a new database, containing 64 types of freshwater macroinvertebrates, ranging in number of images per category from 7 to 577. The database is divided into three datasets, varying in number of categories (64, 29, and 9 categories). Furthermore, in order to accomplish a baseline evaluation performance, we present the classification results of Convolutional Neural Networks (CNNs) that are widely used for deep learning tasks in large databases. Besides CNNs, we experimented with several other well-known classification methods using deep features extracted from the data.The authors would like to thank the Academy of Finland for the grants nos. 288584 , 289076 , and 289104 funding the DETECT consortium's project (Advanced Computational and Statistical Techniques for Biomonitoring and Aquatic Ecosystem Service Management)

Data from: Y chromosome haplotype distribution of brown bears (Ursus arctos) in Northern Europe provides insight into population history and recovery (Ursus arctos)

High-resolution, male-inherited Y-chromosomal markers are a useful tool for population genetic analyses of wildlife species, but to date have only been applied in this context to relatively few species besides humans. Using nine Y-chromosomal STR and three Y-chromosomal single nucleotide polymorphism markers (Y-SNPs), we studied whether male gene flow was important for the recent recovery of the brown bear (Ursus arctos) in Northern Europe, where the species declined dramatically in numbers and geographic distribution during the last centuries but is expanding now. We found 36 haplotypes in 443 male extant brown bears from Sweden, Norway, Finland and Northwestern Russia. In 14 individuals from southern Norway from 1780 to 1920, we found two Y chromosome haplotypes present in the extant population as well as four Y chromosome haplotypes not present among the modern samples. Our results suggested major differences in genetic connectivity, diversity, and structure between the eastern and the western populations in Northern Europe. In the west, our results indicated that the recovered population originated from only four male lineages, displaying pronounced spatial structuring suggestive of large-scale population size increase under limited male gene flow within the western subpopulation. In the east, we found a contrasting pattern, with high haplotype diversity and admixture. This first population genetic analysis of male brown bears shows conclusively that male gene flow was not the main force of population recovery