Examinando por Autor "Recio, Gustavo"

Mostrando 1 - 14 de 14

A Study of Severe Disruption in an Artificial Economy
(2020) Recio, Gustavo; Banzhaf, Wolfgang; White, Roger
We analyze an artificial economy model designed to handle severe non-equilibrium situations. This agent-based model is intended to allow innovation in the form of new technologies, producers and consumers entering (and leaving) the system. Here we examine a disruption of consumption patterns akin to the economic crisis brought about in the real economy through the corona virus and the following Covid-19 pandemic.
AntBot: Ant Colonies for Video Games
(2012) Recio, Gustavo; Martín, Emilio; Estébanez, César; Sáez, Yago
The video game industry is an emerging market which continues to expand. From its early beginning, developers have focused mainly on sound and graphical applications, paying less attention to developing game bots or other kinds of nonplayer characters (NPCs). However, recent advances in artificial intelligence offer the possibility of developing game bots which are dynamically adjustable to several difficulty levels as well as variable game environments. Previous works reveal a lack of swarm intelligence approaches to develop these kinds of agents. Considering the potential of particle swarm optimization due to its emerging properties and self-adaptation to dynamic environments, further investigation into this field must be undertaken. This research focuses on developing a generic framework based on swarm intelligence, and in particular on ant colony optimization, such as it allows general implementation of real-time bots that work over dynamic game environments. The framework has been adapted to allow the implementation of intelligent agents for the classical game Ms. Pac-Man. These were trialed at the Ms. Pac-Man competitions held during the 2011 International Congress on Evolutionary Computation.
Automatic Design of Non Cryptographic Hash Functions Using Genetic Programming
(Willey, 2014-11) Estébanez, César; Sáez, Yago; Recio, Gustavo; Isasi, Pedro
Noncryptographic hash functions have an immense number of important practical applications owing to their powerful search properties. However, those properties critically depend on good designs: Inappropriately chosen hash functions are a very common source of performance losses. On the other hand, hash functions are difficult to design: They are extremely nonlinear and counterintuitive, and relationships between the variables are often intricate and obscure. In this work, we demonstrate the utility of genetic programming (GP) and avalanche effect to automatically generate noncryptographic hashes that can compete with state-of-the-art hash functions. We describe the design and implementation of our system, called GP-hash, and its fitness function, based on avalanche properties. Also, we experimentally identify good terminal and function sets and parameters for this task, providing interesting information for future research in this topic. Using GP-hash, we were able to generate two different families of noncryptographic hashes. These hashes are able to compete with a selection of the most important functions of the hashing literature, most of them widely used in the industry and created by world-class hashing experts with years of experience.
Bi-criterion Optimisation for Configuring an Assembly Supply Chain using Pareto Ant Colony Meta-heuristic
(Elsevier, 2014) Moncayo-Martínez, Luis Antonio; Recio, Gustavo
An assembly supply chain (SC) is composed of stages that provide the components, assemble both sub-assemblies and final products, and deliver products to the customer. The activities carried out in each stage could be performed by one or more options, thus the decision-maker must select the set of options that minimises the cost of goods sold (CoGS) and the lead time (LT), simultaneously. In this paper, an ant colony-based algorithm is proposed to generate a set of SC configurations using the concept of Pareto optimality. The pheromones are updated using an equation that is a function of the CoGS and LT. The algorithm is tested using a notebook SC problem, widely used in literature. The results show that the ratio between the size of the Pareto Front computed by the proposed algorithm and the size of the one computed by exhaustive enumeration is 90%. Other metrics regarding error ratio and generational distance are provided as well as the CPU time to measure the performance of the proposed algorithm.
Efficient dynamic resampling for dominance-based multiobjective evolutionary optimization
(Taylor & Francis Online, 2017) Cervantes, Alejandro; Quintana, David; Recio, Gustavo
Multi-objective optimization problems are often subject to the presence of objectives that require expensive resampling for their computation. This is the case for many robustness metrics, which are frequently used as an additional objective that accounts for the reliability of specific sections of the solution space. Typical robustness measurements use resampling, but the number of samples that constitute a precise dispersion measure has a potentially large impact on the computational cost of an algorithm. This article proposes the integration of dominance based statistical testing methods as part of the selection mechanism of evolutionary multi-objective genetic algorithms with the aim of reducing the number of fitness evaluations. The performance of the approach is tested on five classical benchmark functions integrating it into two well-known algorithms, NSGA-II and SPEA2.
Evolutionary Robustness Analysis for Multi-Objective Optimisation: Benchmark Problems
(2014) Gaspar-Cunha, António; Ferreira, Jose; Recio, Gustavo
From Dynamics to Novelty: An Agent-Based Model of the Economic System
(MIT Press, 2022) Recio, Gustavo; Banzhaf, Wolfgang; White, Roger
The modern economy is both a complex self-organizing system and an innovative, evolving one. Contemporary theory, however, treats it essentially as a static equilibrium system. Here we propose a formal framework to capture its complex, evolving nature. We develop an agent-based model of an economic system in which firms interact with each other and with consumers through market transactions. Production functions are represented by a pair of von Neumann technology matrices, and firms implement production plans taking into account current price levels for their inputs and output. Prices are determined by the relation between aggregate demand and supply. In the absence of exogenous perturbations the system fluctuates around its equilibrium state. New firms are introduced when profits are above normal, and are ultimately eliminated when losses persist. The varying number of firms represents a recurrent perturbation. The system thus exhibits dynamics at two levels: the dynamics of prices and output, and the dynamics of system size. The model aims to be realistic in its fundamental structure, but is kept simple in order to be computationally efficient. The ultimate aim is to use it as a platform for modeling the structural evolution of an economic system. Currently the model includes one form of structural evolution, the ability to generate new technologies and new products.
Learning levels of mario AI using genetic algorithms
(2015) Baldominos, Alejandro; Sáez, Yago; Recio, Gustavo; Calle, Javier
Managing inventory levels and time to market in assembly supply chains by swarm intelligence algorithms
(2016) Moncayo-Martínez, Luis Antonio; Ramírez-López, Adán; Recio, Gustavo
An assembly supply chain (SC) is composed of stages that provide the components, assemble both sub-assemblies and final products, and deliver products to the customer. The activities carried out in each stage could be performed by one or more options, thus the decision-maker must select the set of options that minimises the cost of goods sold (CoGS) and the lead time (LT), simultaneously. In this paper, an ant colony-based algorithm is proposed to generate a set of SC configurations using the concept of Pareto optimality. The pheromones are updated using an equation that is a function of the CoGS and LT. The algorithm is tested using a notebook SC problem, widely used in literature. The results show that the ratio between the size of the Pareto Front computed by the proposed algorithm and the size of the one computed by exhaustive enumeration is 90%. Other metrics regarding error ratio and generational distance are provided as well as the CPU time to measure the performance of the proposed algorithm.
Micromagnetic Modelling on Magnetisation Dynamics with Lossy Magnetic Material in Thin Film Heads by FDTD Computations
(2012) Recio, Gustavo; Estébanez, César
An extension to the standard FDTD formulation aimed at modelling the micromagnetics of materials together with the solution of Maxwell’s equations is presented in this paper. Numerical computations using actual thin film head geometries were carried out with the purpose of validating the method. The analysis of results revealed the importance of the method for modelling electromagnetic interaction with lossy magnetic material in the presence of current and magnetic sources.
Minimising Safety Stock and Lead Time in Production Systems Under Guaranteed-Service Time Models by Swarm Intelligence
(Springer, 2016) Moncayo-Martínez, Luis A.; Zhang, David Z.; Recio, Gustavo
In this chapter, we address the problem of placing safety and in-transit inventory over a multi-stage manufacturing supply chains (SC) in which one or more products are manufactured, subject to a stochastic demand . The first part of the problem is to configure the SC given that manufacturers have one or more options to perform every supplying, assembly, and delivery stage. Then, a certain amount of inventory should be placed on each stage to ensure products are delivered to customers just in the stages’ service time. We tested a new nature-inspired swarm-based meta-heuristic called Intelligent Water Drop (IWD) which imitates some of the processes that happen in nature between the water drops of a river and the soil of the river bed. The proposed approach is based on the creation of artificial water drops, which adapt to their environment to find the optimum path from a river/lake to the sea. This idea is embedded into our proposed algorithm to find the cheapest cost of supplying components, assembling, and delivering products subject to the stages’ service time. We tested our approach using four instances, used widely as test bed in literature. We compared the results computed to the ones computed by Ant Colony Meta-heuristic and provided some metrics as well as graphical results of the outputs.
Performance of the Most Common Non-Cryptographic Hash Functions
(John Wiley & Sons, 2014) Estébanez, César; Sáez, Yago; Recio, Gustavo; Isasi, Pedro
Non-cryptographic hash functions (NCHFs) have an immense number of applications, ranging from compilers and databases to videogames and computer networks. Some of the most important NCHF have been used by major corporations in commercial products. This practical success demonstrates the ability of hashing systems to provide extremely efficient searches over unsorted sets. However, very little research has been devoted to the experimental evaluation of these functions. Therefore, we evaluated the most widely used NCHF using four criteria as follows: collision resistance, distribution of outputs, avalanche effect, and speed. We identified their strengths and weaknesses and found significant flaws in some cases. We also discuss our conclusions regarding general hashing considerations such as selection of the compression map. Our results should assist practitioners and engineers in making more informed choices regarding which function to use for a particular problem
Solving Clustering Problems Using Bi-Objective Evolutionary Optimisation and Knee Finding Algorithms
(2013-06) Recio, Gustavo; Deb, Kalyanmoy
This paper proposes the use of knee finding methods to solve cluster analysis problems from a multi-objective approach. The above proposal arises as a result of a bi-objective study of clustering problems where knee regions on the obtained Pareto-optimal fronts were observed. With increased noise in the data, these knee regions tend to get smoother but still comprise the preferred solution. Thus, being the knees what decision makers are interested in when analysing clustering problems, it makes sense to boost the search towards those regions by applying knee finding techniques.
Using multiobjective optimization algorithms and decision making support to solve polymer extrusion problems
(Wiley, 2018) Denysiuk, Roman; Recio, Gustavo; Covas, José António; Gaspar-Cunha, António
Single screw extrusion is a major polymer processingoperation. Its optimization is crucial for producing goodquality products at suitable costs. This study addressesextrusion as a multiobjective optimization problem thatcan be solved using evolutionary algorithms incorporatingdecision making and robustness strategies for selectingsolutions. This approach enables focusing the search forsolutions in favored regions where the preference wasdefined either by the relative importance of the objectivesor determined considering the robustness of solutionsagainst perturbations in the design variables. The out-come of this strategy provides not only a better insightinto the problem at hand, but also facilitates the choice ofa single solution for practical implementation. The useful-ness of the approach is illustrated by several case studiesinvolving the definition of the most adequate operatingconditions, of the best screw geometry and the twotogether.