Arturo Hernández

A Boltzmann based estimation of distribution algorithm

This paper introduces a new approach for estimation of distribution algorithms called the Boltzmann Univariate Marginal Distribution Algorithm (BUMDA). It uses a Normal-Gaussian model to approximate the Boltzmann distribution, hence, formulae for computing the mean and variance parameters of the Gaussian model are derived from the analytical minimization of the Kullback-Leibler divergence. The resulting formulae explicitly introduces information about the fitness landscape for the Gaussian parameters computation, in consequence, the Gaussian distribution obtains a better bias to sample intensively the most promising regions than simply using the maximum likelihood estimator of the selected set. In addition, the BUMDA formulae needs only one user parameter. Accordingly to the experimental results, the BUMDA excels in its niche of application. We provide theoretical, graphical and statistical analysis to show the BUMDA performance contrasted with state of the art EDAs.

A Set of Test Cases for Performance Measures in Multiobjective Optimization

Comparing the performance of different evolutive multiobjective algorithms is an open problem. With time, many performance measures have been proposed. Unfortunately, the evaluations of many of these performance measures disagree with the common sense of when a multiobjective algorithm is performing better than another. In this work we present a benchmark that is helpful to check if a performance measure actually has a good behavior. Some of the most popular performance measures in literature are tested. The results are valuable for a better understanding of what performance measures are better.

Analysis of Cyber-bullying in a virtual social networking

This paper focuses on the social and cultural implications of cyber technologies. Identity, bullying and inappropriate use of communication are major issues that need to be addressed in relation to communication technologies for the security in the Web use. The contribution of this paper is to present a novel approach to explain the performance of a novel Cyber-bullying model applied on a Social Network using Multiagents to improve the understanding of this social behavior.

Artificial Societies and Social Simulation using Ant Colony, Particle Swarm Optimization and Cultural Algorithms

The proposal of this chapter is to explain the implementation of collective intelligent techniques to improve results in artificial societies and social simulation using diverse concepts such as argumentation, negotiation and reputation models to improve social simulation of artificial societies implementing dioramas, and multivariable analysis in different application domains for example Logistics. These techniques will be useful for answering diverse queries after gathering general information about a given topic. This kind of collective intelligence will be characterized by: ant colony, particle swarm optimization, and cultural algorithms, each one of these implementing diverse models or agents for simulate a social behaviour. Intelligent agents are used to obtain information to take decisions that try to improve the heuristic optimization needed in different application and fields of knowledge. First, in section 1 of this paper, we approach different concepts related with Artificial Societies and Social Simulation using different strategies to analyze and model the necessary information to support the correct decisions of the evolving models. In other sections we explain the way to generate a specific behaviour with collective-intelligence techniques –ant colony (section 2), particle swarm optimization (section 3), and cultural algorithms (section 4). In section 5 we apply this knowledge in diverse fields and application domains that needs a heuristic optimization and the more innovative perspectives of each technique. In

Bat algorithm to improve a Financial Trust Forest

A controversial topic and frequently in public policy analysis is related with temporality in projects with limited funds associated with natural resources. Public resources can be organized as a Financial Trust. Very often the relationship between the budgets requested and can be received is overwhelming, as it is very unlikely to be as necessary as that can be awarded. In addition, strategic approaches, political and ecological considerations permeate the decision-making on such assignments. To meet these regulatory criteria, underlying any prevailing public policy or government ideology, it is clear that both must be appropriate to prioritize the development projects in ecological project portfolios, these must be consistent with principles sound (for example, maximization of social benefits in the future). Computation so using novel bioinspired algorithms (In this case a Bat Algorithm) can be characterized as follows: · They can be no doubt profitable, but its benefits are indirect, perhaps only in the long run may be visible and difficult to quantify. · Apart from its potential contribution to economic welfare, are not intangible benefits in the present, which must be considered to achieve a holistic view of their ecological and social impact. · Equity in relation to the magnitude of the impact of a specific project and social conditions of the beneficiaries should also be considered. In the present study was conducted using an approach to intelligent optimization problem for a Financial Trust Forest in Chihuahua.

Efficient Estimation of Distribution Algorithms by using the Empirical Selection Distribution

Estimation of Distribution Algorithms (EDAs) (Muhlenbein et al., 1996; Muhlenbein & PaaB, 1996) are a promising area of research in evolutionary computation. EDAs propose to create models that can capture the dependencies among the decision variables. The widely known Genetic Algorithm could benefit from the available dependencies if the building blocks of the solution were correlated. However, it was proved that the building blocks of a genetic algorithm have a limited capacity for discovering and using complex relationships (correlations) among variables. EDAs instead, focus on learning probability distributions which serve as the vehicle to capture the data dependencies and the data structure as well. In order to show how the proposed method unifies the theory for infinite sized population with the finite sized population case of practical EDAs, we explain them first. An EDA with infinite sized population would perform the steps shown in the algorithm in Table 1.

Social Data Mining to Improve Bioinspired Intelligent Systems

The proposal of this chapter is to explain the implementation of social data mining to improve results in bioinspired intelligent systems using generation of clusters, associative rules; decision trees, associated models, dioramas and multivariable analysis for obtain knowledge about any issue related with a topic. This kind of intelligent systems using bioinspired computing – specially, group intelligence techniques such as: Ant Colony, Particle Swarm Optimization and Cultural Algorithmsthat try to simulate biological processes that occur in the nature. Intelligent agents use this information to make decisions to improve a needed heuristic optimization in different fields such as: negotiation, argumentation or artificial societies simulation. First in section 2 of this chapter, we approach different concepts related with social data mining and how to use different ways to analyze and model the necessary information to support the correct decision of agents; in next three sections we explain the way to generate a specific behaviour by using group intelligence techniques –ant colony (section 3), particle swarm optimization (section 4) and cultural algorithms (section 5), In section 6, we apply this knowledge in diverse fields and application domains that use a heuristic optimization. In section 7 we compare different cases of studies: Eurovision Voting problem, and the Distribution of Elements. Finally in section 8 we provide our conclusions and outline our future research.

Resolution of a Combinatorial Problem using Cultural Algorithms

Many problems involve not structured environments which can be solved from the perspective of Bioinspired Algorithms (Cultural Algorithms). In this paper, a proposed algorithm is used to resolve a famous game known as Japanese puzzles, which are analyzed for obtain the optimal solution. The authors show that Japanese Puzzles are constrained combinatorial optimization problems, which can be solved using Cultural Algorithms. Other features, such the use of a belief space involve many proposed solutions and local search heuristics; can also be taught using these puzzles.

G-indicator: an m-ary quality indicator for the evaluation of non-dominated sets

Due to the big success of the Paretos Optimality Criteria for multi-objective problems, an increasing number of algorithms that use it have been proposed. The goal of these algorithms is to find a set of non-dominated solutions that are close to the True Pareto front. As a consequence, a new problem has arisen, how can the performance of different algorithms be evaluated? In this paper, we present a novel system to evaluate m non-dominated sets, based on a few assumptions about the preferences of the decision maker. In order to evaluate the performance of our approach, we build several test cases considering different topologies of the Pareto front. The results are compared with those of another popular metric, the S-metric, showing equal or better performance.

Hybrid System to Determine the Ranking of a Returning Participant in Eurovision

Many problems involve not structured environments which can be solved from the perspective of particle swarm optimization (PSO). In this research analyze the voting behavior in a popular song contest held every year in Europe. The dataset makes it possible to analyze the determinants of success, and gives a rare opportunity to run a direct test of vote trading from logrolling. We show that they are rather driven by linguistic and cultural proximities between singers and voting countries. With this information it is possible to predict the score of a new country, redistributed the assigned votes for a lot of the participants, this paper tries to explain this social behavior.