Meeting Young Researchers


Aguirre Hernan

Research Area:
Computational Intelligence, Soft Computing
Evolutionary Computation, Multidisciplinary Design Optimization, Many Objectives Optimization

Employment Experience:
Apr. 1990 - Sep. 1992:
National Polytechnic School of Ecuador, Laboratory Assistant

Oct. 1992 - Feb. 1994:
Ormaco - InteleQ Inc., Software Engineer

Jun. 1994 - Mar. 1997:
Innovateq, Computer Systems Consultant

Apr. 1994 - Aug. 1995:
National Polytechnic School of Ecuador, Lecturer

Mar. 1996 - Mar. 1997:
Pontifical Catholic University of Ecuador, Lecturer

Apr. 2003 - Mar. 2005:
Shinshu University, Post Doctoral Research Fellow

Apr. 2005 - Mar. 2007:
Shinshu University, Research Associate

Apr. 2007 - Oct. 2007:
Shinshu University, Assistant Professor

Nov. 2007 -:
Shinshu University, Fiber-Nanotech International Young Researchers Empowerment Project, Tenure-track Assistant Professor

Mar. 1990
National Polytechnic School of Ecuador, Computer Systems Engineer

Sep. 1997
Hitotsubashi University, Japanese Courses (since Apr. 1997)

Mar. 2000
Shinshu University, Master of Engineering

Mar. 2003
Shinshu University, Doctor of Engineering

Sep. 2005
Grant by the Wakasato-kai, Nagano

Mar. 2003
Best Paper Award Nomination, European Workshop on Evolutionary Computation in Combinatorial Optimization, Essex

Jul. 2002
Student Grant by the American Association for Artificial Intelligence - GECCO 2002, New York

Jul. 2001
Student Grant by the American Association for Artificial Intelligence - GECCO 2001, San Francisco

Jul. 1999
Student Grant by the American Association for Artificial Intelligence - GECCO 1999, Orlando

Nov. 1999
Student Grant by NEC, Foundation for Computer & Communications Promotion, Tokyo

Oct. 1999
Student Grant by IEEE International Conference on Systems Man & Cybernetics, Tokyo

Apr. 1997
Research Scholarship, Ministry of Sports, Culture, and Education of Japan (until Mar. 2003)

Dec. 1992
Engineer Degree Cum Laude, National Polytechnic School, Quito

Selected Publications:
  • - Hernan Aguirre, Masahiko Sato and Kiyoshi Tanaka, "δ-Similar Elimination to Enhance Search Performance of Multiobjective Evolutionary Algorithms", IEICE Trans. Information and Systems, vol.E91-D, no.4, pp.1206-1210, Apr. 2008.
  • - Kiyoshi Tanaka and Hernan Aguirre, "Halftone Image Generation using Evolutionary Computation", Genetic and Evolutionary Computation in Image Processing and Computer Vision, Editors S. Cagnoni, E. Lutton, G. Olague, EURASIP Book Series on Signal Processing and Communications, Hindawi Publishing Corp., Volume 8, chapter 4, pp.65-92, 2007.
  • - H. Aguirre and K. Tanaka, "Working Principles, Behavior, and Performance of MOEAs on MNK-Landscapes", European Journal of Operational Research, Elsevier, vol. 181, pp. 1670-1690, 2007.
  • - H. Sato, H. Aguirre, and K. Tanaka,
    " Local Dominance and Local Recombination in MOEAs on 0/1 Multiobjective Knapsack Problems",
    European Journal of Operational Research, Elsevier, vol. 181, pp. 1708-1723, 2007.
  • - H. Aguirre and K. Tanaka,
    " A Model for Parallel Operators in Genetic Algorithms",
    Parallel Evolutionary Computations, Editors N. Nedjah, E. Alba, and L. Mourelle, Springer, Studies in Computational Intelligence, vol. 22, chapter 1, pp.3-31, 2006.
  • - H. Aguirre and K. Tanaka,
    " Random Bit Climbers on Multiobjective MNK-Landscapes: Effects of Memory and Population Climbing",
    IEICE Trans. Fundamentals, vol.E88-A, no.1, pp.334-345, Jan. 2005.
  • - E. Myodo, H. Aguirre, and K. Tanaka,
    " Inter-Block Evaluation Method to Further Reduce Evaluation Numbers in GA-Based Image Halftoning Technique",
    IEICE Trans. Fundamentals, vol.E87-A, no.10, pp.2722-2731, Oct. 2004.
  • - H. Aguirre and K. Tanaka,
    " A Study on Parallel Varying Mutation in Deterministic and Self-Adaptive GAs with 0/1 Multiple Knapsack Problems",
    IPSJ Trans. Mathematical Modeling and its Applications, vol.45, no.SIG 2(TOM10),
    pp.77-90, Feb. 2004.
  • - H. Aguirre and K. Tanaka, "A Study on the Behavior of Genetic Algorithms on NK-Landscapes: Effects of Selection, Drift, Mutation, and Recombination", IEICE Trans. Fundamentals, vol.E86-A, no.9, pp.2270-2279, Sep. 2003.
  • - H. Aguirre, K. Tanaka, and S. Oshita, "Performance Study of a Distributed Genetic Algorithm with Parallel Cooperative-Competitive Genetic Operators", IEICE Trans. Fundamentals, vol.E85-A, no.9, pp.2083-2088, Sep.2002.
  • - T. Umemura, H. Aguirre, and K. Tanaka, "Multi-level Image Halftoning Technique with Genetic Algorithm", IEICE Trans. Fundamentals, vol.E85-A, no.8, pp.1892-1897, Aug. 2002.
  • - H. Aguirre, K. Tanaka, T. Sugimura, and S. Oshita, "Simultaneous Halftone Image Generation with Improved Multiobjective Genetic Algorithm", IEICE Trans. Fundamentals, vol.E84-A, no.8, pp.1869-1882, Aug. 2001.
  • - H. Aguirre, K. Tanaka, and T. Sugimura, "Accelerated Image Halftoning Technique Using Improved Genetic Algorithm", IEICE Trans. Fundamentals, vol.E83-A, no.8, pp.1566-1574, Aug. 2000.
  • - H. Aguirre, K. Tanaka, and T. Sugimura, "Empirical Model with Cooperative-Competitive Genetic Operators to Improve GAs: Performance Investigation with 0/1 Multiple Knapsack Problems", IPSJ Journal, vol.41, no.10, pp.2837-2851, Oct. 2000.

Research Statement

My research is focused on fundamentals and applications of computational intelligence approaches that mimic nature for problems solving. I am particularly interested in evolutionary computation methods, which are computer-based systems that use computational models of biological evolutionary processes as a key element of their design. Some well known evolutionary techniques are evolutionary algorithms, swarm intelligence, artificial immune systems, and differential evolution. Widely known evolutionary algorithms are genetic algorithms, evolutionary programming, evolution strategies, genetic programming, and learning classifier systems.

One of the fastest growing application areas of evolutionary computation is multi-objective optimization, where evolutionary approaches have been successfully applied to solve multi-criteria optimization problems, especially in the case of two and three objectives problems. Engineering design problems can often be conveniently formulated as multi-criteria optimization problems and there is a growing interest in applying evolutionary algorithms to solve them. However, these problems often consist of a relatively large number of objectives (many objectives), constrained spaces, and non-linear correlations among the variables we want to optimize (epistasis).

An important aim of my work is the development of evolutionary algorithms that can operate effectively in many objectives spaces with non-linear landscapes. The key to a successful design of this new generation of algorithms is a research effort that includes work on fundamentals of evolutionary computation to best match the evolutionary principles with classes of highly dimensional problem we aim to solve. Population search, solutions ranking, selection, and genetic operators are all important issues that need to be analyzed carefully in non-linear fitness landscapes of high dimensional spaces in order to achieve best performance.

Research on fundamentals includes:
- Find ways to appropriately perform selection, recombination, and mutation in multiple and many objectives landscapes
- Understand the effects of epistasis in multiple and many objectives optimization
- Parallelization of evolutionary algorithms

Applications I am working on include:
- Information security
- Automatic generation of cryptographically strong functions
- Computer networks intrusion detection
- Image and video processing for information hiding and authentication

I am also interested on applying evolutionary principles to
- Data mining and classification
- Model inference
- Circuits & systems design and synthesis

My goal:

The tenure track position gives us the resources and the opportunity to pursue our own line of research independently. My hope for the next years is to build on this unique opportunity to lay the foundations and work to establish a world leading laboratory in the field of computational intelligence. Regarding education, I am looking forward to help educating the next generation of leaders in engineering at Shinshu University.