Data Mining Practical Machine Learning Tools and Techniques

Multi-objective optimization using genetic algorithms: A tutorial

Corruption and Growth

Real-life decisions are usually made in the state of uncertainty such as randomness and fuzziness. How do we model optimization problems in uncertain environments? How do we solve these models? In order to answer these questions, this book provides a self-contained, comprehensive and up-to-date presentation of uncertain programming theory, including numerous modeling ideas, hybrid intelligent algorithms, and applications in system reliability design, project scheduling problem, vehicle routing problem, facility location problem, and machine scheduling problem. Researchers, practitioners and students in operations research, management science, information science, system science, and engineering will find this work a stimulating and useful reference.

Theory and practice of uncertain programming

Ten years of genetic fuzzy systems: current framework and new trends

Solving fuzzy solid transportation problems by an evolutionary algorithm based parametric approach

Uncertain solid transportation problems

A methodology for energy multivariate time series forecasting in smart buildings based on feature selection

In this paper we analyse the causes of the Spanish property model and its territorial, social, and political consequences. Particular attention is paid to sociopolitical contexts. These consequences include excessive dependence on economic activity and employment in the housing construction sector, the irreversible disappearance of landmarks in the country's collective history and culture, and examples of ‘policy capture’, especially at local and regional levels. This lengthy process has led to corruption in town planning and an increase in poor policy decisions, greatly harming Spain's reputation.

(Un)Sustainable Territories: Causes of the Speculative Bubble in Spain (1996–2010) and its Territorial, Environmental, and Sociopolitical Consequences

The paper follows the line of the design and evaluation of new evolutionary algorithms for constrained multi-objective optimization. The evolutionary algorithm proposed (ENORA) incorporates the Pareto concept of multi-objective optimization with a constraint handling technique and with a powerful diversity mechanism to obtain multiple nondominated solutions through the simple run of the algorithm. Constraint handling is carried out in an evolutionary way and using the min-max formulation, while the diversity technique is based on the partitioning of search space in a set of radial slots along which are positioned the successive populations generated by the algorithm. A set of test problems recently proposed for the evaluation of this kind of algorithm has been used in the evaluation of the algorithm presented. The results obtained with ENORA were very good and considerably better than those obtained with algorithms recently proposed by other authors.

Fernando Jiménez

Papers

Solving fuzzy solid transportation problems by an evolutionary algorithm based parametric approach

Uncertain solid transportation problems

A methodology for energy multivariate time series forecasting in smart buildings based on feature selection

(Un)Sustainable Territories: Causes of the Speculative Bubble in Spain (1996–2010) and its Territorial, Environmental, and Sociopolitical Consequences

An evolutionary algorithm for constrained multi-objective optimization