Antonio Latorre

Universidad Politécnica de Madrid | UPM · Departamento de Arquitectura y Tecnología de Sistemas Informáticos

In this paper we present an algorithm to segment the nuclei of neuronal cells in confocal microscopy images, a key technical problem in many experimental studies in the field of neuroscience. We describe the whole procedure, from the original images to the segmented individual nuclei, paying particular attention to the binarization of the images, w...

Evolutionary Algorithms are powerful optimization techniques which have been applied to many different problems, from complex mathematical functions to real-world applications. Some studies report performance improvements through the combination of different evolutionary approaches within the same hybrid algorithm. However, the mechanisms used to c...

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 performanc...

Evolutionary Algorithms have shown during the last decades that they can solve a wide range of real-world problems in different fields, such as science or engineering. In this paper, we explore the application of Hybrid Evolutionary Algorithms (a Differential Evolution algorithm plus a tailor-made local search called MLS) to the generative design o...

Heuristic optimisation is a popular tool for solving problems in the sciences and engineering fields. These algorithms explore the search space by sampling solutions, evaluating their fitness, and biasing the search in the direction of promising solutions. However, in many cases, this fitness function involves executing expensive computational calc...

The military environment generates a large amount of data of great importance, which makes necessary the use of machine learning for its processing. Its ability to learn and predict possible scenarios by analyzing the huge volume of information generated provides automatic learning and decision support. This paper aims to present a model of a machi...

The tourism sector is undergoing an accelerated digital transformation, augmented by the current pandemic, and tourists must adapt to this new environment. There are many options for digitalisation in the tourism sector and their success depends on the grade of tourist satisfaction. Not all options work and, from a marketing point of view, it is im...

Bio-inspired optimization (including Evolutionary Computation and Swarm Intelligence) is a growing research topic with many competitive bio-inspired algorithms being proposed every year. In such an active area, preparing a successful proposal of a new bio-inspired algorithm is not an easy task. Given the maturity of this research field, proposing a...

(250max) Background Analyzing images to accurately estimate the number of different cell types in the brain using automatic methods is a major objective in neuroscience. The automatic and selective detection and segmentation of neurons would be an important step in neuroanatomical studies. New Method: We present a method to improve the 3D reconstr...

In the last few years, the formulation of real-world optimization problems and their efficient solution via metaheuristic algorithms has been a catalyst for a myriad of research studies. In spite of decades of historical advancements on the design and use of metaheuristics, large difficulties still remain in regards to the understandability, algori...

Bio-inspired optimization (including Evolutionary Computation and Swarm Intelligence) is a growing research topic with many competitive bio-inspired algorithms being proposed every year. In such an active area, preparing a successful proposal of a new bio-inspired algorithm is not an easy task. Given the maturity of this research field, proposing a...

Neuronal damage, in the form of both brain and spinal cord injuries, is one of the major causes of disability and death in young adults worldwide. One way to assess the direct damage occurring after a mechanical insult is the simulation of the neuronal cells functional deficits following the mechanical event. In this study, we use a coupled electro...

With the widespread adoption of data mining models to solve real-world problems, the scientific community is facing the need of increasing their interpretability and comprehensibility. This is especially relevant in the case of black box models, in which inputs and outputs are usually connected by highly complex and nonlinear functions; in applicat...

The bottleneck for progress in many research areas within neuroscience has shifted from the data acquisition to the data analysis stages. In the present article, we propose a method named InTool Explorer that we have developed to perform interactive exploratory data analysis, focusing on neuroanatomy as an example of its utility. This tool is freel...

While the finite element method (FEM) has now reached full maturity both in academy and industry, its use in optimisation pipelines remains either computationally intensive or cumbersome. In particular, currently used optimisation schemes leveraging FEM still require the choice of dedicated optimisation algorithms for a specific design problem, and...

Over the recent years, continuous optimization has significantly evolved to become the mature research field it is nowadays. Through this process, evolutionary algorithms had an important role, as they are able to obtain good results with limited resources. Among them, bio-inspired algorithms, which mimic cooperative and competitive behaviors obser...

Large Scale Global Optimization is one of the most active research lines in evolutionary and metaheuristic algorithms. In the last five years, several conference sessions and journal special issues have been conducted, and many algorithmic alternatives and hybrid methods, more and more sophisticated, have been proposed. However, most of the propose...

Synapses are key elements in the information transmission in the nervous system. Among the different approaches to study them, the use of computational simulations is identified as the most promising technique. Simulations, however, do not provide generalized models of the underlying biochemical phenomena, but a set of observations, or time-series...

These days, transportation and logistic problems in large cities are demanding smarter transportation services that provide flexibility and adaptability. A possible solution to this arising problem is to compute the best routes for each new scenario. In this problem, known in the literature as the dial-a-ride problem, a number of passengers are tra...

This paper presents a mechanism to generate vir-tual buildings considering designer constraints and guidelines. This mechanism is implemented as a pipeline of different Variable Neighborhood Search (VNS) optimization processes in which sev-eral subproblems are tackled (1) rooms locations, (2) connectivity graph, and (3) element placement. The core...

In this paper, we present an algorithm to create 3D segmentations of neuronal cells from stacks of previously segmented 2D images. The idea behind this proposal is to provide a general method to reconstruct 3D structures from 2D stacks, regardless of how these 2D stacks have been obtained. The algorithm not only reuses the information obtained in t...

In this contribution we present a study on the combination of Differential Evolution and the IPOP-CMA-ES algorithms. The hybrid algorithm has been constructed by using the Multiple Offspring Sampling framework, which allows the seamless combination of multiple metaheuristics in a dynamic algorithm capable of adjusting the participation of each of t...

On-demand transportation is becoming a new necessary service for modern (public and private) mobility and logistics providers. Large cities are demanding more and more share transportation services with flexible routes, resulting from user dynamic demands. In this study a new algorithm is proposed for solving the problem of computing the best route...

Although procedural and assisted content generation have attracted a lot of attention in both academic and industrial research in video games, there are few cases in the literature in which they have been applied to sport management games. The on-line variants of these games produce a lot of information concerning how the users interact with each o...

Continuous optimization is one of the most active research Iines in evolutionary and metaheuristic algorithms. Through CEC 2005 to CEC 2013 competitions, many different algorithms have been proposed to solve continuous problems. The advances on this type of problems are of capital importance as many real-world problems from very different domains (...

Continuous optimization is one of the most active research lines in evolutionary and metaheuristic algorithms. Through CEC 2005 to CEC 2011 competitions, many different algorithms have been proposed to solve continuous problems. The advances on this type of problems are of capital importance as many real-world problems from very different domains (...

Hybrid Evolutionary Algorithms are a promising alternative to deal with the problem of selecting the most appropriate Evolutionary Algorithm for a specific problem. By means of the combination of different heuristic optimization approaches, it is possible to profit from the benefits of the best approach or, even more, to discover synergies between...

One of the most promising areas in which probabilistic graphical models have shown an incipient activity is the field of heuristic optimization and, in particular, in Estimation of Distribution Algorithms. Due to their inherent parallelism, different research lines have been studied trying to improve Estimation of Distribution Algorithms from the p...

The P300 component of the brain event-related-potential is one of the most used signals in brain computer interfaces (BCIs). One of the required steps for the application of the P300 paradigm is the identification of this component in the presence of stimuli. In this paper we propose a direct optimization approach to the P300 classification problem...

Neurotransmitters used by chemical synapses are stored in synaptic vesicles that accumulate in axon terminals. The number and position of these vesicles have been related to some important functional properties of the synapse. For this reason, an accurate mechanism for semi-automatically counting these small cellular structures will be of great hel...

Continuous optimization is one of the most active research lines in evolutionary and metaheuristic algorithms. Through CEC 2005 to CEC 2010 competitions, many different algorithms have been proposed to solve continuous problems. The advances on this type of problems are of capital importance as many real-world problems from very different domains (...

Evolutionary Algorithms (EAs) are a set of optimization techniques that have become incredibly popular in the last decades. As they are general purpose algorithms, they have been applied to a wide range of problems, many of them from industrial or scientific disciplines. Several approaches have been proposed, each of them implementing the biologica...

In this paper, a hybrid algorithm based on the Multiple Offspring Sampling framework is presented and benchmarked on the BBOB-2010 noisy testbed. MOS allows the seamless combination of multiple metaheuristics in a hybrid algorithm capable of dynamically adjusting the participation of each of the composing algorithms. The experimental results show a...

In this contribution, a hybrid algorithm combining Differential Evolution and IPOP-CMA-ES is presented and benchmarked on the BBOB 2010 noiseless testbed. The hybrid algorithm has been constructed within the Multiple Offspring Sampling framework, which allows the seamless combination of multiple metaheuristics in a dynamic algorithm capable of adju...

The selection of the most appropriate Evolutionary Algorithm for a given optimization problem is a difficult task. Hybrid Evolutionary Algorithms are a promising alternative to deal with this problem. By means of the combination of different heuristic optimization approaches, it is possible to profit from the benefits of the best approach, avoiding...

Estimation of distribution algorithms (EDAs) are one of the most promising paradigms in today’s evolutionary computation. In this field, there has been an incipient activity in the so-called parallel estimation of distribution algorithms (pEDAs). One of these approaches is the distributed estimation of distribution algorithms (dEDAs). This paper in...

The study conducted in this work analyses the interactions between different Evolutionary Algorithms when they are hybridized. For this purpose, the phylogenetic tree of the best solution reported by the hybrid algorithm is reconstructed, and the relationships among the ancestors of this solution are established. For each of these ancestors, the ev...

One of the most promising areas in which probabilistic graphical models have shown an incipient activity is the field of heuristic optimization and, in particular, in the Estimation of Distribution Algorithms (EDAs). EDAs constitute a well-known family of Evolutionary Computation techniques, similar to Genetic Algorithms. Due to their inherent para...

Continuous optimization is one of the most active research lines in evolutionary and metaheuristic algorithms. Since CEC 2005 and CEC 2008 competitions, many different algorithms have been proposed to solve continuous problems. Despite there exist very good algorithms reporting high quality results for a given dimension, the scalability of the sear...

Evolutionary Algorithms (EAs) are powerful metaheuristics that can be applied to almost any optimization problem. However, different Evolutionary Algorithms own different search capabilities that make them more suitable for one or another optimization problem. Furthermore, the combination of several EAs can boost the performance of individual appro...

This paper addresses a way to generate mixed strategies using reinforcement learning algorithms in domains with stochastic rewards. A new algorithm, based on Q-learning model, called TERSQ is introduced. As a difference from other approaches for stochastic scenarios, TERSQ uses a global exploration rate for all the state/actions in the same run. Th...

Multiple offspring sampling (MOS) is a hybrid algorithm where different evolutionary approaches can coexist simultaneously. The algorithm dynamically evaluates the quality of the solutions produced by each of these algorithms (or techniques, as they are called within MOS) and adjusts their participation in the overall evolutionary process according...

Scheduling is a very important problem in many real-world scenarios. In the case of supercomputers it is even more important because available resources are limited and expensive. The optimal use of supercomputer facilities is a critical question. We have found that the definitions of traditional scheduling problems do not provide an appropriate de...

Deceptive problems have always been considered difficult for Genetic Algorithms. To cope with this characteristic, the literature has proposed the use of Parallel Genetic Algorithms (PGAs), particularly multi-population island-based models. Although the existence of multiple populations encourages population diversity, these problems are still diff...

The correct choice of an evolutionary algorithm, a genetic representation for the problem being solved (as well as their associated variation operators) and the appropriate values for the parameters of the algorithm is a hard task and it is often considered as an optimization problem itself. In this contribution, we propose a new theoretical formal...

1. Abstract Composite materials are combinations of, at least, two organic or inorganic materials, working to- gether to give the composite some desired properties. Composites are highly-used on industrial design (3). Their light weight make them key elements to reduce weight and direct operating costs in some domains like aeronautics. Genetic algo...

Parallel genetic algorithms (PGAs) are a powerful tool to deal with complex optimization problems. Nevertheless, the task of selecting its parameters accurately is an optimization problem by itself. Any additional help or hints to adjust the configuration parameters will lead both towards a more efficient PGA application and to a better comprehensi...

Web page clustering is one of the major preprocessing steps in web mining analysis. In data mining, preprocessing is a key task to ensure reliabil- ity and quality of the knowledge extracted by the whole mining process. As the amount of data to process is potentially infinite if dynamic web pages are con- sidered, the need of preprocessing this inf...