Josu Ceberio

Josu Ceberio
Universidad del País Vasco / Euskal Herriko Unibertsitatea | UPV/EHU · Computer Sciences and Artificial Intelligence

PhD on Computer Science
Associate Editor of the IEEE Transactions on Evolutionary Computation

About

86
Publications
15,999
Reads
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1,021
Citations
Additional affiliations
October 2017 - February 2019
Universidad del País Vasco / Euskal Herriko Unibertsitatea
Position
  • Professor (Assistant)
December 2010 - present
Universidad del País Vasco / Euskal Herriko Unibertsitatea
Position
  • PhD Student

Publications

Publications (86)
Preprint
Full-text available
Neural Combinatorial Optimization (NCO) is an emerging domain where deep learning techniques are employed to address combinatorial optimization problems as a standalone solver. Despite their potential, existing NCO methods often suffer from inefficient search space exploration, frequently leading to local optima entrapment or redundant exploration...
Conference Paper
Neural Combinatorial Optimization (NCO) is an emerging domain where deep learning techniques are employed to address combinatorial optimization problems as a standalone solver. Despite their potential, existing NCO methods often suffer from inefficient search space exploration, frequently leading to local optima entrapment or redundant exploration...
Preprint
Full-text available
Most Reinforcement Learning (RL) environments are created by adapting existing physics simulators or video games. However, they usually lack the flexibility required for analyzing specific characteristics of RL methods often relevant to research. This paper presents Craftium, a novel framework for exploring and creating rich 3D visual RL environmen...
Article
Probability-based algorithms have proven to be a solid alternative for approaching optimization problems. Nevertheless, in many cases, using probabilistic models that efficiently exploit the characteristics of the problem involves large computational overheads, and therefore, lower complexity models such as those that are univariate are usually emp...
Article
Full-text available
Experimentation is an intrinsic part of research since it allows for collecting quantitative observations, validating hypotheses, and providing evidence for their reformulation. For that reason, experimentation must be coherent with the purposes of the research, properly addressing the relevant questions in each case. Unfortunately, the literature...
Article
Neural Combinatorial Optimization has emerged as a new paradigm in the optimization area. It attempts to solve optimization problems by means of neural networks and reinforcement learning. In the last few years, due to their novelty and presumably good performance, many research papers have been published introducing new neural architectures for a...
Article
Full-text available
We analyze three permutation-based combinatorial optimization problems in Fourier space, namely, the Quadratic Assignment Problem, the Linear Ordering Problem and the symmetric and non-symmetric Traveling Salesperson Problem. In previous studies, one can find a number of theorems with necessary conditions that the Fourier coefficients of the aforem...
Preprint
Full-text available
p>The energy consumption of Artificial Intelligence (AI) systems has increased 300,000-fold from 2012 to now, and data centers running massive AI software produce up to 5-9% of global electricity demand and 2% of all CO2 emissions. Such an increase in energy consumption has been partially motivated by the strong development of new AI-specific archi...
Preprint
Full-text available
p>The energy consumption of Artificial Intelligence (AI) systems has increased 300,000-fold from 2012 to now, and data centers running massive AI software produce up to 5-9% of global electricity demand and 2% of all CO2 emissions. Such an increase in energy consumption has been partially motivated by the strong development of new AI-specific archi...
Article
Global random search algorithms are characterized by using probability distributions to optimize problems. Among them, generative methods iteratively update the distributions by using the observations sampled. For instance, this is the case of the well-known Estimation of Distribution Algorithms. Although successful, this family of algorithms itera...
Article
Full-text available
Recent advances in graph neural network (GNN) architectures and increased computation power have revolutionized the field of combinatorial optimization (CO). Among the proposed models for CO problems, neural improvement (NI) models have been particularly successful. However, the existing NI approaches are limited in their applicability to problems...
Preprint
An experimental comparison of two or more optimization algorithms requires the same computational resources to be assigned to each algorithm. When a maximum runtime is set as the stopping criterion, all algorithms need to be executed in the same machine if they are to use the same resources. Unfortunately, the implementation code of the algorithms...
Preprint
Full-text available
Problems with solutions represented by permutations are very prominent in combinatorial optimization. Thus, in recent decades, a number of evolutionary algorithms have been proposed to solve them, and among them, those based on probability models have received much attention. In that sense, most efforts have focused on introducing algorithms that a...
Article
The field of dynamic optimisation continuously designs and compares algorithms with adaptation abilities that deal with changing problems during their search process. However, restarting the search algorithm after a detected change is sometimes a better option than adaptation, although it is generally ignored in empirical studies. In this paper, we...
Article
In the field of optimization and machine learning, the statistical assessment of results has played a key role in conducting algorithmic performance comparisons. Classically, null hypothesis statistical tests have been used. However, recently, alternatives based on Bayesian statistics have shown great potential in complex scenarios, especially when...
Article
Full-text available
In this paper, a direct approach is developed for discovering optimal transfer trajectories of close-range rendezvous of satellites considering disturbances in elliptical orbits. The control vector representing the inputs is parameterized via different interpolation methods, and an Estimation of Distribution Algorithm (EDA) that implements mixtures...
Article
Full-text available
Handling non-linear constraints in continuous optimization is challenging, and finding a feasible solution is usually a difficult task. In the past few decades, various techniques have been developed to deal with linear and non-linear constraints. However, reaching feasible solutions has been a challenging task for most of these methods. In this pa...
Article
Full-text available
In recent decades, Estimation of Distribution Algorithms (EDAs) have gained much popularity in the evolutionary computation community for solving optimization problems. Characterized by the use of probabilistic models to represent the solutions and the interactions between the variables of the problem, EDAs can be applied to either discrete, contin...
Chapter
Fitness landscape rotation has been widely used in the field of dynamic combinatorial optimisation to generate test problems with academic purposes. This method changes the mapping between solutions and objective values, but preserves the structure of the fitness landscape. In this work, the rotation of the landscape in the combinatorial domain is...
Article
Full-text available
Non-deterministic measurements are common in real-world scenarios: the performance of a stochastic optimization algorithm or the total reward of a reinforcement learning agent in a chaotic environment are just two examples in which unpredictable outcomes are common. These measures can be modeled as random variables and compared among each other via...
Preprint
Full-text available
In recent years, Deep Learning based methods have been a revolution in the field of combinatorial optimization. They learn to approximate solutions and constitute an interesting choice when dealing with repetitive problems drawn from similar distributions. Most effort has been devoted to investigating neural constructive methods, while the works th...
Preprint
Full-text available
Neural Combinatorial Optimization attempts to learn good heuristics for solving a set of problems using Neural Network models and Reinforcement Learning. Recently, its good performance has encouraged many practitioners to develop neural architectures for a wide variety of combinatorial problems. However, the incorporation of such algorithms in the...
Preprint
Full-text available
Non-deterministic measurements are common in real-world scenarios: the performance of a stochastic optimization algorithm or the total reward of a reinforcement learning agent in a chaotic environment are just two examples in which unpredictable outcomes are common. These measures can be modeled as random variables and compared among each other via...
Article
Full-text available
In this article, an interactive tool for simulation of satellites dynamics and autonomous spacecraft guidance is presented. Different geopotential models for orbit propagation of Earth-orbiting satellites are provided, which consider earth's gravitational field with various accuracies. The presented software is a 3-D visualization platform for spac...
Article
Full-text available
The Quadratic Assignment Problem (QAP) is a well-known permutation-based combinatorial optimization problem with real applications in industrial and logistics environments. Motivated by the challenge that this NP-hard problem represents, it has captured the attention of the optimization community for decades. As a result, a large number of algorith...
Preprint
This paper presents a framework to tackle constrained combinatorial optimization problems using deep Reinforcement Learning (RL). To this end, we extend the Neural Combinatorial Optimization (NCO) theory in order to deal with constraints in its formulation. Notably, we propose defining constrained combinatorial problems as fully observable Constrai...
Article
Network Function Virtualization (NFV) introduces a new network architecture framework that evolves network functions, traditionally deployed over dedicated equipment, to software implementations that run on general-purpose hardware. One of the main challenges for deploying NFV is the optimal resource placement of demanded network services in the NF...
Article
It is an old claim that, in order to design a (meta)heuristic algorithm for solving a given optimization problem, algorithm designers need first to gain a deep insight into the structure of the problem. Nevertheless, in recent years, we have seen an incredible rise of “new” meta-heuristic paradigms that have been applied to any type of optimization...
Preprint
The Quadratic Assignment Problem (QAP) is a well-known permutation-based combinatorial optimization problem with real applications in industrial and logistics environments. Motivated by the challenge that this NP-hard problem represents, it has captured the attention of the evolutionary computation community for decades. As a result, a large number...
Article
Full-text available
In this paper, an approach is presented for finding the optimal long-range space rendezvous in terms of fuel and time, considering limited impulse. In this approach , the Lambert problem is expanded towards a discretized multi-impulse transfer. Taking advantage of an analytical form of multi-impulse transfer, a feasible solution that satisfies the...
Conference Paper
Full-text available
Dynamic optimisation problems (DOPs) are optimisation problems that change over time. Typically, DOPs have been defined as a sequence of static problems, and the dynamism has been inserted into existing static problems using different techniques. In the case of dynamic permutation problems, this process has been usually done by the rotation of the...
Conference Paper
The Quadratic Assignment Problem (QAP) is a specially challenging permutation-based np-hard combinatorial optimization problem, since instances of size n > 40 are seldom solved using exact methods. In this sense, many approximate methods have been published to tackle this problem, including Estimation of Distribution Algorithms (EDAs). In particula...
Conference Paper
The most commonly used statistics in Evolutionary Computation (EC) are of the Wilcoxon-Mann-Whitney-test type, in its either paired or non-paired version. However, using such statistics for drawing performance comparisons has several known drawbacks. At the same time, Bayesian inference for performance analysis is an emerging statistical tool, whic...
Conference Paper
Full-text available
In this paper, a direct approach is presented to tackle the multi-impulse rendezvous problem considering the impulse limit. Particularly, the standard Lambert problem is extended toward several consequential orbit transfers for the rendezvous problem. A number of different evolutionary algorithms are taken into consideration. It is shown that the p...
Preprint
Full-text available
Neural networks are gaining popularity in the reinforcement learning field due to the vast number of successfully solved complex benchmark problems. In fact, artificial intelligence algorithms are, in some cases, able to overcome human professionals. Usually, neural networks have more than a couple of hidden layers, and thus, they involve a large q...
Article
Full-text available
This article is a survey paper on solving spacecraft trajectory optimization problems. The solving process is decomposed into four key steps of mathematical modeling of the problem, defining the objective functions, development of an approach and obtaining the solution of the problem. Several subcategories for each step have been identified and des...
Article
Permutation problems are combinatorial optimization problems whose solutions are naturally codified as permutations. Due to their complexity, motivated principally by the factorial cardinality of the search space of solutions, they have been a recurrent topic for the artificial intelligence and operations research community. Recently, among the vas...
Conference Paper
Full-text available
The flowshop scheduling problem (FSP) has been widely studied in the last decades, both in the single objective as well as in the multi-objective scenario. Besides, due to the real-world considerations on scheduling problems, the concern regarding sequence-dependent setup times has emerged. In this paper, we present a decomposition-based iterated l...
Conference Paper
The statistical assessment of the empirical comparison of algorithms is an essential step in heuristic optimization. Classically, researchers have relied on the use of statistical tests. However, recently, concerns about their use have arisen and, in many fields, other (Bayesian) alternatives are being considered. For a proper analysis, different a...
Conference Paper
Estimation of distribution algorithms have already demonstrated their utility when solving a broad range of combinatorial problems. However, there is still room for methodological improvements when approaching constrained type problems. The great majority of works in the literature implement external repairing or penalty schemes, or use ad-hoc samp...
Article
In the last decades, the permutation flowshop scheduling problem (PFSP) has been thoroughly studied in combinatorial optimization and scheduling research. The most common objectives for this problem are makespan, total flowtime, and total tardiness. Furthermore, most production scheduling problems naturally involve multiples conflicting objectives...
Article
Full-text available
Konputazio ebolutiboan, algoritmoek optimizazio-problemen gainean duten errendimendua ebaluatzeko ohikoa izaten da problema horien hainbat instantzia erabiltzea. Batzuetan, problema errealen instantziak eskuragarri daude, eta beraz, esperimentaziorako instantzien multzoa hortik osatzen da. Tamalez, orokorrean, ez da hori gertatzen. Instantziak esku...
Article
In the last decade, many works in combinatorial optimisation have shown that, due to the advances in multi-objective optimisation, the algorithms from this field could be used for solving single-objective problems as well. In this sense, a number of papers have proposed multi-objectivising single-objective problems in order to use multiobjective al...
Conference Paper
Full-text available
In space environment, perturbations make the spacecraft lose its predefined orbit in space. One of these undesirable changes is the in-plane rotation of space orbit, denominated as orbital precession. To overcome this problem, one option is to correct the orbit direction by employing low-thrust trajectories. However, in addition to the orbital pert...
Conference Paper
The Boltzmann distribution plays a key role in the field of optimization as it directly connects this field with that of probability. Basically, given a function to optimize, the Boltzmann distribution associated to this function assigns higher probability to the candidate solutions with better quality. Therefore, an efficient sampling of the Boltz...
Conference Paper
In the last decade, many works in combinatorial optimisation have shown that, due to the advances in multi-objective optimisation, the algorithms in this field could be used for solving single-objective problems. In this sense, a number of papers have proposed multi-objectivising single-objective problems in order to apply multi-objectivisation sch...
Conference Paper
Recently, distance-based exponential probability models, such as Mallows and Generalized Mallows, have demonstrated their validity in the context of estimation of distribution algorithms (EDAs) for solving permutation problems. However, despite their successful performance, these models are unimodal, and therefore, they are not flexible enough to a...
Article
The Mallows (MM) and the Generalized Mallows (GMM) probability models have demonstrated their validity in the framework of Estimation of distribution algorithms (EDAs) for solving permutation-based combinatorial optimisation problems. Recent works, however, have suggested that the performance of these algorithms strongly relies on the distance used...
Article
The Linear Ordering Problem is a popular combinatorial optimisation problem which has been extensively addressed in the literature. However, in spite of its popularity, little is known about the characteristics of this problem. This paper studies a procedure to extract static information from an instance of the problem, and proposes a method to inc...
Conference Paper
Recently, probability models on rankings have been proposed in the field of estimation of distribution algorithms in order to solve permutation-based combinatorial optimisation problems. Particularly, distance-based ranking models, such as Mallows and Generalized Mallows under the Kendall's-τ distance, have demonstrated their validity when solving...
Conference Paper
The Linear Ordering Problem is a combinatorial optimization problem which has been frequently addressed in the literature due to its numerous applications in diverse fields. In spite of its popularity, little is known about its complexity. In this paper we analyze the linear ordering problem trying to identify features or characteristics of the ins...
Conference Paper
Estimation of distribution algorithms are known as powerful evolutionary algorithms that have been widely used for diverse types of problems. However, they have not been extensively developed for permutation-based problems. Recently, some progress has been made in this area by introducing probability models on rankings to optimize permutation domai...
Article
The aim of this paper is two-fold. First, we introduce a novel general estimation of distribution algorithm to deal with permutation-based optimization problems. The algorithm is based on the use of a probabilistic model for permutations called the generalized Mallows model. In order to prove the potential of the proposed algorithm, our second aim...
Article
Estimation of distribution algorithms (EDAs) are a set of algorithms that belong to the field of Evolutionary Computation. Characterized by the use of probabilistic models to represent the solutions and the dependencies between the variables of the problem, these algorithms have been applied to a wide set of academic and real-world optimization pro...
Conference Paper
Estimation of Distribution Algorithms are a set of algorithms that belong to the field of Evolutionary Computation. Characterized by the use of probabilistic models to learn the (in)dependencies between the variables of the optimization problem, these algorithms have been applied to a wide set of academic and real-world optimization problems, achie...
Conference Paper
Estimation of Distribution Algorithms are a class of evolutionary algorithms characterized by the use of probabilistic models. These algorithms have been applied successfully to a wide set of artificial and real-world problems, achieving competitive results in most scenarios. Nevertheless, there are some problems whose solutions can be naturally re...
Conference Paper
Full-text available
Pervasive computing calls for applications which are often composed from independent and distributed components using facilities from the environment. This paradigm has evolved into task based computing where the application composition relies on explicit user task descriptions. The composition of applications has to be performed at run-time as the...
Article
Full-text available
Pervasive environments are characterized by a large number of resources and services that are used by applications to perform tasks of the user. The availability of these resources is often limited by physical constraints, such as computational capacity, available memory, or bandwidth. Moreover, pervasive environments enable user-centric applicatio...
Article
Full-text available
Task-based computing paradigm enables new kinds of mobile applications which are composed by networking services and heterogeneous resources. These applications use explicit user task descriptions to bind resources at runtime, thus making it easier to achieve application adaptation in changing context. In this paper, we will present an algorithm fo...

Questions

Question (1)
Question
For years, we have seen papers being published that make us wonder about the direction that research is taking. Perhaps it is time to stop and think a little about the type of research we want to do. In the attached paper, we include some personal thoughts related to this in the field of metaheuristic optimization.

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