BIOCOMPUTING AND DEVELOPMENT SYSTEMS LAB

Birthplace of Grammatical Evolution

Biography

Enrique Naredo is a Postdoctoral Researcher at the Biocomputing and Developmental Systems (BDS) research group at the University of Limerick (UL), which is part of Lero. He is an expert in Grammatical Evolution (GE) commonly used powerful automatic programming technique proudly invented by BDS Group, which is used in over 4,000 scientific papers and referenced in ten patents. Enrique is the senior postdoctoral researcher on the Automatic Design of Digital Circuits (ADDC) project, a 1.8 million euro project with partners including Intel, S3 and Emdahlo. He supervises PhD students within the BDS as well as MSc students enrolled in the Ireland’s First Industry-Driven Masters in Artificial Intelligence at UL. Enrique has authored 24 conference and journal papers.

Email: Enrique.Naredo@ul.ie

 

Research Interests

He is an enthusiastic collaborator in doing research mainly focused on evolutionary computing to solve regression, clustering, classification, computer vision and robot navigation tasks, working with a prestigious researcher’s network from well known Universities and Research Centers among them from Lisbon, Portugal; Bourdeaux, France; Merida, Spain; Tijuana, Mexico, and Limerick, Ireland.

  • Evolutionary Computing
  • Grammatical Evolution
  • Novelty Search

Publications

1.

Searching for novel clustering programs

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2.

Searching for novel regression functions - IEEE Conference Publication

The objective function is the core element in most search algorithms that are used to solve engineering and scientific problems, referred to as the fitness

3.

Searching for Novel Classifiers

Part of the Lecture Notes in Computer Science book series (LNCS, volume 7831) Natural evolution is an open-ended search process without an a priori fitness function that needs to be optimized. On the other hand, evolutionary algorithms (EAs) rely on a clear and quantitative objective.

4.

ACO-Tuning of a Fuzzy Controller for the Ball and Beam Problem

We describe the use of Ant Colony Optimization (ACO) for the ball and beam control problem, in particular for the problem of tuning a fuzzy controller of the Sugeno type. In our case study the...

5.

A Comparison of Fitness-Case Sampling Methods for Symbolic Regression with Genetic Programming

The canonical approach towards fitness evaluation in Genetic Programming (GP) is to use a static training set to determine fitness, based on a cost function averaged over all fitness-cases. However,...

6.

Preliminary Study of Bloat in Genetic Programming with Behavior-Based Search

Leonardo Trujillo Enrique Naredo Yuliana Martínez Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 227) Bloat is one of the most interesting theoretical problems in genetic programming (GP), and one of the most important pragmatic limitations in the development of real-world GP solutions.

7.

Generalization in Maze Navigation Using Grammatical Evolution and Novelty Search

Recent research on evolutionary algorithms has begun to focus on the issue of generalization. While most works emphasize the evolution of high quality solutions for particular problem instances,...

8.

NEAT, There's No Bloat

The Operator Equalization (OE) family of bloat control methods have achieved promising results in many domains. In particular, the Flat-OE method, that promotes a flat distribution of program sizes,...

9.

A behavior-based analysis of modal problems

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11.

RANSAC-GP: Dealing with Outliers in Symbolic Regression with Genetic Programming

Uriel López Leonardo Trujillo Yuliana Martinez Pierrick Legrand Enrique Naredo Sara Silva Part of the Lecture Notes in Computer Science book series (LNCS, volume 10196) Genetic programming (GP) has been shown to be a powerful tool for automatic modeling and program induction. It is often used to solve difficult symbolic regression tasks, with many examples in real-world domains.

12.

EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation II | SpringerLink

This book comprises a selection of papers from the EVOLVE 2012 held in Mexico City, Mexico. The aim of the EVOLVE is to build a bridge between probability, set oriented numerics and evolutionary compu

13.

A comparison of fitness-case sampling methods for genetic programming

A comparison of fitness-case sampling methods for genetic programming

14.

The training set and generalization in grammatical evolution for autonomous agent navigation

Over recent years, evolutionary computation research has begun to emphasize the issue of generalization. Instead of evolving solutions that are optimized for a particular problem instance, the goal...

15.

Novelty Search for the Synthesis of Current Followers

A topology synthesis method is introduced using genetic algorithms (GA) based on novelty search (NS). NS is an emerging meta-heuristic, that guides the search based on the novelty of each solution instead of the objective function. The synthesized topologies are current follower (CF) circuits; these topologies are new and designed using integrated circuit CMOS technology of 0.35 μ xm.

16.

Evolving genetic programming classifiers with novelty search

Novelty Search (NS) is a unique approach towards search and optimization, where an explicit objective function is replaced by a measure of solution no...

17.

Evolutionary Approach for Detection of Buried Remains Using Hyper...: Ingenta Connect

BP) for automating the design of Hyperspectral Visual Attention Models (H-VAM.), which is proposed as a new method for the detection of buried remains. Four graves were simulated and monitored during six months by taking in situ spectral measurements of the ground.

18.

Genetic Programming Based on Novelty Search

Novelty Search (NS) is a unique approach towards search and optimization, where an explicit objective function is replaced by a measure of solution novelty. However, NS has been mostly used in evolutionary robotics, its usefulness in classic machine learning problems has been unexplored.

19.

20.

Disparity Map Estimation by Combining Cost Volume Measures Using Genetic Programming

Stereo vision is one of the most active research areas in modern computer vision. The objective is to recover 3-D depth information from a pair of 2-D images that capture the same scene. This paper...

  • 6 PhDs
  • 3 Post Docs
  • 2 Faculty Member
  • 1 Softwared Developer
  • 15 PhDs Completed
  • More than 200 Publications
  • Multidisciplinary Projects
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