A
Anna I. Esparcia-Alcázar
Researcher at Polytechnic University of Valencia
Publications - 56
Citations - 1253
Anna I. Esparcia-Alcázar is an academic researcher from Polytechnic University of Valencia. The author has contributed to research in topics: Evolutionary algorithm & Genetic programming. The author has an hindex of 11, co-authored 56 publications receiving 1219 citations. Previous affiliations of Anna I. Esparcia-Alcázar include University of Strathclyde & University of Glasgow.
Papers
More filters
Book
Applications of Evolutionary Computing
Mario Giacobini,Anthony Brabazon,Stefano Cagnoni,Gianni A. Di Caro,Anikó Ekárt,Anna I. Esparcia-Alcázar,Muddassar Farooq,Andreas Fink,Penousal Machado +8 more
TL;DR: EvoCOMNET Contributions.- Web Application Security through Gene Expression Programming, Location Discovery in Wireless Sensor Networks Using a Two-Stage Simulated Annealing, and more.
Book ChapterDOI
A Genetic Programming Approach for Bankruptcy Prediction Using a Highly Unbalanced Database
TL;DR: The application of a genetic programming algorithm to the problem of bankruptcy prediction using a database of Spanish companies and improving those obtained with the support vector machine is presented.
Journal ArticleDOI
Bloat control operators and diversity in genetic programming: A comparative study
TL;DR: A comparison of several bloat control methods and also evaluates a recent proposal for limiting the size of the individuals: a genetic operator called prune and plant, which has demonstrated to be better in terms of fitness, size reduction, and time consumption than any of the other bloat Control techniques under comparison.
Journal ArticleDOI
Fitness approximation for bot evolution in genetic programming
TL;DR: The success of the fitness approximation by similarity estimation method for bot evolution in UT2K4 allows us to expect similar results in environments that share the same characteristics.
Book ChapterDOI
Testing the Intermediate Disturbance Hypothesis: Effect of Asynchronous Population Incorporation on Multi-Deme Evolutionary Algorithms
Juan J. Merelo,Antonio M. Mora,Pedro A. Castillo,Juan Luis Jiménez Laredo,Lourdes Araujo,Ken Sharman,Anna I. Esparcia-Alcázar,Eva Alfaro-Cid,Carlos Cotta +8 more
TL;DR: The Intermediate Disturbance Hypothesis states that a moderate population disturbance results in the maximum ecological diversity, and experiments performed on two combinatorial optimization problems show that the highest algorithmic effect is produced if it is done in the middle of the evolution of the first population.