Neural network-based colonoscopic diagnosis using on-line learning and differential evolution

In this paper, on-line training of neural networks is investigated in the context of computer-assisted colonoscopic diagnosis. A memory-based adaptation of the learning rate for the on-line back-propagation (BP) is proposed and used to seed an on-line evolution process that applies a differential evolution (DE) strategy to (re-) adapt the neural network to modified environmental conditions. Our approach looks at on-line training from the perspective of tracking the changing location of an approximate solution of a pattern-based, and thus, dynamically changing, error function. The proposed hybrid strategy is compared with other standard training methods that have traditionally been used for training neural networks off-line. Results in interpreting colonoscopy images and frames of video sequences are promising and suggest that networks trained with this strategy detect malignant regions of interest with accuracy

Time-stamped resampling for robust evolutionary portfolio optimization

Traditional mean–variance financial portfolio optimization is based on two sets of parameters, estimates for the asset returns and the variance–covariance matrix. The allocations resulting from both traditional methods and heuristics are very dependent on these values. Given the unreliability of these forecasts, the expected risk and return for the portfolios in the efficient frontier often differ from the expected ones. In this work we present a resampling method based on time-stamping to control the problem. The approach, which is compatible with different evolutionary multiobjective algorithms, is tested with four different alternatives. We also introduce new metrics to assess the reliability of forecast efficient frontiers.The authors acknowledge financial support granted by the Spanish Ministry of Science under contract TIN2008-06491-C04- 03 (MSTAR), TIN2011-28336 (MOVES) and Comunidad de Madrid (CCG10-UC3M/TIC-5029).Publicad

Improved Wolf Pack Algorithm for Optimum Design of Truss Structures

In order to find a more effective method in structural optimization, an improved wolf pack optimization algorithm was proposed. In the traditional wolf pack algorithm, the problem of falling into local optimum and low precision often occurs. Therefore, the adaptive step size search and Levy's flight strategy theory were employed to overcome the premature flaw of the basic wolf pack algorithm. Firstly, the reasonable change of the adaptive step size improved the fineness of the search and effectively accelerated the convergence speed. Secondly, the search strategy of Levy's flight was adopted to expand the search scope and improved the global search ability of the algorithm. At last, to verify the performance of improved wolf pack algorithm, it was tested through simulation experiments and actual cases, and compared with other algorithms. Experiments show that the improved wolf pack algorithm has better global optimization ability. This study provides a more effective solution to structural optimization problems

Multiobjective Algorithms with Resampling for Portfolio Optimization

Constrained financial portfolio optimization is a challenging domain where the use of multiobjective evolutionary algorithms has been thriving over the last few years. One of the major issues related to this problem is the dependence of the results on a set of parameters. Given the nature of financial prediction, these figures are often inaccurate, which results in unreliable estimates for the efficient frontier. In this paper we introduce a resampling mechanism that deals with uncertainty in the parameters and results in efficient frontiers that are more robust. We test this idea on real data using four multiobjective optimization algorithms (NSGA-II, GDE3, SMPSO and SPEA2). The results show that resampling significantly increases the reliability of the resulting portfolios

Recent Trends in the Use of Statistical Tests for Comparing Swarm and Evolutionary Computing Algorithms: Practical Guidelines and a Critical Review

A key aspect of the design of evolutionary and swarm intelligence algorithms is studying their performance. Statistical comparisons are also a crucial part which allows for reliable conclusions to be drawn. In the present paper we gather and examine the approaches taken from different perspectives to summarise the assumptions made by these statistical tests, the conclusions reached and the steps followed to perform them correctly. In this paper, we conduct a survey on the current trends of the proposals of statistical analyses for the comparison of algorithms of computational intelligence and include a description of the statistical background of these tests. We illustrate the use of the most common tests in the context of the Competition on single-objective real parameter optimisation of the IEEE Congress on Evolutionary Computation (CEC) 2017 and describe the main advantages and drawbacks of the use of each kind of test and put forward some recommendations concerning their use.Spanish Ministry of Economy, Industry and CompetitivenessSpanish Ministry of Scienc

Swarm Robotics

This study analyzes and designs the Swarm intelligence (SI) that Self-organizing migrating algorithm (SOMA) represents to solve industrial practice as well as academic optimization problems, and applies them to swarm robotics. Specifically, the characteristics of SOMA are clarified, shaping the basis for the analysis of SOMA's strengths and weaknesses for the release of SOMA T3A, SOMA Pareto, and iSOMA, with outstanding performance, confirmed by well-known test suites from IEEE CEC 2013, 2015, 2017, and 2019. Besides, the dynamic path planning problem for swarm robotics is handled by the proposed algorithms considered as a prime instance. The computational and simulation results on Matlab have proven the performance of the novel algorithms as well as the correctness of the obstacle avoidance method for mobile robots and drones. Furthermore, two out of the three proposed versions achieved the tie for 3rd (the same ranking with HyDE-DF) and 5th place in the 100-Digit Challenge at CEC 2019, GECCO 2019, and SEMCCO 2019 competition, something that any other version of SOMA has yet to do. They show promising possibilities that SOMA and SI algorithms offer.Tato práce se zabývá analýzou a vylepšením hejnové inteligence, kterou představuje samoorganizující se migrační algoritmus s možností využití v průmyslové praxi a se zaměřením na hejnovou robotiku. Je analyzován algoritmus SOMA, identifikovány silné a slabé stránky a navrženy nové verze SOMA jako SOMA T3A, SOMA Pareto, iSOMA s vynikajícím výkonem, potvrzeným známými testovacími sadami IEEE CEC 2013, 2015, 2017 a 2019. Tyto verze jsou pak aplikovány na problém s dynamickým plánováním dráhy pro hejnovou robotiku. Výsledky výpočtů a simulace v Matlabu prokázaly výkonnost nových algoritmů a správnost metody umožňující vyhýbání se překážkám u mobilních robotů a dronů. Kromě toho dvě ze tří navržených verzí dosáhly na 3. a 5. místo v soutěži 100-Digit Challenge na CEC 2019, GECCO 2019 a SEMCCO 2019, což je potvrzení navržených inovací. Práce tak demonstruje nejen vylepšení SOMA, ale i slibné možnosti hejnové inteligence.460 - Katedra informatikyvyhově

Schémas de valorisation économique de grands ensembles de chauffe-eau électriques coordonnés par un agrégateur au sein d'un réseau intelligent

La vitamine D3 est un nutriment lipophile. Elle a été identifiée pour la première fois au 20ème siècle pour son implication dans la survenue du rachitisme. Elle est considérée comme une pro-hormone de la famille des stéroïdes. Ces dernières décennies, la vitamine D3 a attiré l’attention de nombreux chercheurs due à ses effets non seulement squelettiques (maintenance de la bonne minéralisation d’os) mais aussi extra-squelettiques comme la prévention de cancer, de diabète et même la réduction de taux de mortalité. L’utilisation de la nanotechnologie dans le domaine pharmaceutique a suscité un intérêt considérable ces dernières années, notamment l’utilisation de nanovecteurs pour la délivrance d’agents thérapeutiques avec une meilleure biodisponibilité en diminuant leurs effets secondaires. Parmi ces nanovecteurs, on trouve les nanoémulsions sous forme des nanocapsules lipidiques (LNCs). L’axe de recherche de ce mémoire est l’utilisation des nanocapsules lipidiques comme plateforme pour la délivrance de la vitamine D3. Un des objectifs est de développer des nanocapsules lipidiques avec une quantité optimale de la vitamine D3 dans 3 triglycérides à chaîne moyenne (TMC) (Labrafac, Tricaprylin et LC-810L). Une étude de stabilité physique a été faite sur des LNCs chargées en vitamine D3 et vides (Placebo) à l’état liquide et après lyophilisation. Par ailleurs, une étude de la cinétique de libération de la vitamine D3 in vitro a été aussi réalisée. A la fin, les résultats ont montré qu’on arrive à solubiliser jusqu’à 10 % p/p de la vitamine D3 dans les trois TCM. En revanche, on a eu des nanoémulsions (chargées en vitamine D3 ainsi que vides) stables qu’avec Labrafac et Tricaprylin durant plus de 6 mois ainsi qu’une stabilité de LNCs lyophilisées durant plus de deux mois. Mots clés : nanoémulsion, nanocapsules lipidiques, méthode d’inversion de phase et vitamine D3.----------ABSTRACT Vitamin D3 is a lipophilic nutrient. It was first identified in the 20th century for its involvement in the onset of rickets. It is considered as a pro-hormone of the steroid family. In recent decades, vitamin D3 has attracted the attention of many researchers due to its effects not only skeletal (maintenance of good bone mineralization) but also extra skeletal as the prevention of cancer and diabetes or even the reduction of mortality rates. The use of nanotechnology in the pharmaceutical field has attracted considerable interest in recent years, including the use of nanovectors for the delivery of therapeutic agents to improve their bioavailability by decreasing their side effects. Among these nanovectors, we find the lipid nanocapsules (in the form of nanoemulsions). The research focus of this paper is the use of lipid nanocapsules as a platform for vitamin D3 delivery. One of the objectives is to develop lipid nanocapsules with an optimal amount of vitamin D3 in 3 oils (Labrafac, Tricaprylin and LC-810L). A physical stability study was conducted on LNCs loaded with vitamin D3 and empty ones (Placebo) in the liquid state and after freeze-drying. In addition, a kinetic study of vitamin D3 release in vitro was also carried out. In the end, the results showed that up to 10% w/w of vitamin D3 was soluble in the 3 oils. On the other hand, we had stable nanoemulsions (loaded with vitamin D3 and empty ones) as with Labrafac and Tricaprylin more than 6 months. The lyophilized LNCs have also showed à good stability after 2 months of the resuspension in water. Key words: nanoemulsion, lipid nanocapsules, phase inversion temperature and vitamin D3