2009

1. Oltean Mihai, Muntean Oana, Solving the subset sum problem with a light-based computer, Natural Computing, Springer-Verlag, Vol 8, Issue 2, pp. 321-331,  2009 [pdf] [website] [Springer]

2. Oltean Mihai, Light-based string matching, Natural Computing, Springer-Verlag, (accepted) [pdf] [Springer]

3. Oana Muntean, Mihai Oltean, Using light for solving the unbounded subset-sum problem, IJICIC, Vol 5, Issue 8, 2159-2167, 2009 [pdf]

4. Oana Muntean, Mihai Oltean, Deciding whether a linear Diophantine equation has solutions by using a light-based device, Journal of Optoelectronics and Advanced Materials, Vol. 11, Issue 11, pp. 1728-1734, 2009 [pdf]

5. Mihai Oltean, Oana Muntean, Evolutionary design of graph-based structures for optical computing, in proceedings of the second workshop on Optical SuperComputing, OSC 2009, LNCS 5882, Springer-Verlag, 2009.

6. Mihai Oltean, Laura Diosan, An autonomous GP-based system for regression and classification problems, Applied Soft Computing, Vol. 9, Issue 1, pp. 49-60, 2009
7. Diosan Laura, Mihai Oltean, Evolutionary design of Evolutionary Algorithms, Genetic Programming and Evolvable Machines, Springer, Vol 10, Issue 3, pp. 263-306, 2009
8. Mihai Oltean, Crina Grosan, Laura Diosan, Cristina Mihaila, Genetic Programming with linear representation: a survey, International Journal on Artificial Intelligence Tools, World Scientific, Vol. 19, Issue 2, pp. 197-238, 2009.

2008

1. Oltean Mihai, Solving the Hamiltonian path problem with a light-based computer, Natural Computing, Springer-Verlag, Vol 6, Issue 1, pp. 57-70, 2008 [pdf] [website] [Springer]

2. Oltean Mihai, Muntean Oana, Exact Cover with Light, New Generation Computing, Springer-Verlag, Vol. 26, Issue 4, pp. 327-344, 2008 [pdf] [website]

3. Mihai Oltean, Oana Muntean, Solving NP-Complete Problems with Delayed Signals: An Overview of Current Research Directions, in proceedings of 1^st international workshop on Optical SuperComputing, LNCS 5172, pp. 115-128, Springer-Verlag, 2008

2007

1. Muntean, Oana; Oltean, Mihai, Processing Bank Checks with Genetic Programming and Histograms, Bio-inspired, Learning, and Intelligent Systems for Security, BLISS 2007, pp. 102-105, IEEE Press, 2007

2. Dioşan, L., Oltean, Mihai, Who's better? PESA or NSGA II? ,The International Conference on Intelligent Systems Design and Applications,  Workshop on Evolutionary Multiobjective Optimization: Design and Applications, Brazil, October 22th - 24th, 2007, pp. 869-874, IEEE CS Press [abstract], [html]

3. Muntean O., Dioşan, L., Oltean, Mihai, Solving the even-n-parity problems using Best Sub Tree Genetic Programming, Adaptive Hardware Systems 2007, pp. 511-518, IEEE Press, 2007 - [abstract],  [html]

4. Muntean O., Dioşan, L., Oltean, M., Best SubTree Genetic Programming, GECCO 2007, pp. 1667  - 1673, ACM Press, 2007.

5. Dioşan, L., Oltean, Mihai, A. Rogozan, J. P. Pecuchet, Genetically Designed Multiple-Kernels for Improving the SVM Performance,  GECCO 2007, pp. 1873, 2007 - [abstract], [html]

6. Dioşan, L., Oltean, Mihai, Evolving Evolutionary Algorithms using Evolutionary Algorithms, GECCO 2007, 2442-2449, ACM Press - [abstract],  [html]

7. Dioşan, L., Oltean, Mihai, Observing the swarm behavior during its evolutionary design,  GECCO 2007, 2667-2674, ACM Press - [abstract],  [html]

8. Oltean Mihai, Evolving Evolutionary Algorithms with Patterns, Soft Computing, Springer-Verlag, Vol. 11, Issue 6, pp. 503-518, 2007  [abstract] [pdf]

9. Oltean Mihai, Grosan C., Using Traceless Genetic Programming for Solving Multiobjective Optimization Problems, Journal of Experimental and Theoretical Artificial Intelligence, Taylor & Francis, Vol. 19, pp. 227-248 [pdf] [Taylor and Francis]

10. Diosan L., Oltean Mihai, Evolving the Update Strategy of the Particle Swarm Optimisation Algorithms, International Journal on Artificial Intelligent Tools, Vol. 16, Issue 1, pp. 87-110, World-Scientific.

11. Oltean Mihai, Liquid State Genetic Programming, International Conference on Adaptive and Natural Computing Algorithms, Warsaw, Poland, ICANNGA 2007, B. Beliczynski et al. (Eds.), Part I, LNCS 4431, Springer-Verlag, pp. 220-229, 2007. [pdf] (best paper award)

12. Diosan L, Oltean Mihai, (et al.), Improving SVM Performance using a Linear Combination of Kernels, International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2007, B. Beliczynski et al. (Eds.), Part II, LNCS 4432, Springer-Verlag, pp. 218-227, 2007.

2006

1. Oltean Mihai, A light-based device for solving the Hamiltonian path problem, Unconventional Computing, Calude C. (et al.)(Eds), LNCS 4135, pp. 217-227, Springer-Verlag, 2006 [abstract], [ppt], [html] [Springer]

2. Oltean Mihai, Switchable Glass: A possible medium for Evolvable Hardware, NASA conference on Adaptive Hardware Systems, Stoica A., et al. (Eds), pp 81-87, IEEE CS Press, 2006 [abstract].

3. Dioşan, L., Oltean, Mihai, Evolving crossover operators for function optimization, European Conference on Genetic Programming, LNCS, Springer-Verlag, pp. 97-108, 2006  (best paper nominee)

4. Dioşan, L.,  Oltean Mihai., Evolving the structure of the Particle Swarm Optimization algorithms, European Conference on Evolutionary Computation in Combinatorial Optimization, LNCS, Springer-Verlag, pp. 25-36, 2006.

2005

1. Oltean Mihai, Evolving Evolutionary Algorithms using Linear Genetic Programming, Evolutionary Computation, MIT Press, Vol. 13, Issue 3, pp. 387-410, 2005. [abstract] [pdf]

2. Grosan C., Oltean Mihai, Adaptive Representation for Single Objective Optimization, Soft Computing, Springer-Verlag, 9(8): 594-605, 2005.

3. Oltean Mihai, Evolving reversible circuits for the even-parity problem, EvoHOT workshop, Lausanne, Switzerland, Applications of Evolutionary Computing, Rothlauf, F.; Branke, J.; Cagnoni, S.; Corne, D.W.; Drechsler, R.; Jin, Y.; Machado, P.; Marchiori, E.; Romero, J.; Smith, G.D.; Squillero, G. (Eds.), LNCS 3449, pp. 225-234, Springer-Verlag, Berlin, 2005.

4. Dumitrescu D., Grosan C., Oltean Mihai, Evolving Continuous Pareto Regions, Evolutionary Computation Based Multi-Criteria Optimization: Theoretical Advances and Applications, edited by A. Abraham, L. Jain and R. Goldberg, Springer-Verlag, London, pp. 167-199, 2005.
    

2004

1. Oltean Mihai, A Practical Evidence for the No Free Lunch Theorems, BioInspired Approaches to Advanced Information Technology, BioADIT'04, Lausanne, Switzerland, 29-31 January, edited by A. Ijspeert (et al), pp. 382-388, 2004.

2. Oltean Mihai, Searching for a Practical Evidence for the No Free Lunch Theorems, BioInspired Approaches to Advanced Information Technology, BioADIT'04, Lausanne, Switzerland, 29-31 January, edited by A. Ijspeert (et al), LNCS 3141, pp. 472-483, Springer-Verlag, Berlin, 2004. [abstract]

3. Oltean Mihai (et al.), Evolving Digital Circuits for the Knapsack Problem, International Conference on Computational Sciences, E-HARD Workshop, 6-9 June, Krakow, Poland, Edited by M. Bubak, G. D. van Albada, P. Sloot, and J. Dongarra, Vol. III, pp. 1257-1264,  Springer-Verlag, Berlin, 2004.

4. Oltean, M., Solving Even-Parity Problems using Traceless Genetic Programming, IEEE Congress on Evolutionary Computation, Portland, 19-23 June, edited by G. Greenwood (et. al), pages 1813-1819, IEEE Press, 2004.

5. Oltean Mihai (et al.), Encoding Multiple Solutions in a Linear GP Chromosome, International Conference on Computational Sciences, E-HARD Workshop, 6-9 June, Krakow, Poland, Edited by M. Bubak, G. D. van Albada, P. Sloot, and J. Dongarra, Vol III, pp. 1281-1288,  Springer-Verlag, Berlin, 2004.

6. Oltean Mihai, Evolving Winning Strategies for Nim-like Games, World Computer Congress, Student Forum, 26-29 August, Toulouse, France, edited by Mohamed Kaaniche, pp. 353-364, Kluwer Academic Publisher, 2004.

7. Oltean Mihai, Solving Classification Problems using Traceless Genetic Programming, World Computer Congress, The Symposium on Professional Practice in AI, 26-29 August, Toulouse, France, edited by E. Mercier-Laurent, J. Debenham, pp. 403-412, 2004.

8. Oltean Mihai, Dumitrescu, D., Evolving TSP Heuristics using Multi Expression Programming, International Conference on Computational Sciences, ICCS'04, 6-9 June, Krakow, Poland, Edited by M. Bubak, G. D. van Albada, P. Sloot, and J. Dongarra, Vol II, pp. 670-673, Springer-Verlag, Berlin, 2004.

9. Oltean, Mihai, Dumitrescu, D., A Permutation based Approach for the 2-D Cutting Stock Problem, First International Industrial Conference Bionik 2004, 22-23 April, Hanover, Germany, Edited by I. Boblan, and R. Bannasch, pp. 73-80, 2004.

10. Oltean Mihai, Improving the Search by Encoding Multiple Solutions in a Chromosome, contributed chapter, Evolutionary Machine Design, pages 85-110, Nova Science Publisher, New-York, edited by Nadia Nedjah (et. al).

11. Oltean Mihai, Improving Multi Expression Programming: an Ascending Trail from Sea-level Even-3-parity Problem to Alpine Even-18-Parity Problem, contributed chapter, Evolvable Machines: Theory and Practice, edited by Nadia Nedjah (et. al), pages 229-255, Springer-Verlag, Berlin, 2004.

12. Oltean Mihai, Grosan C., Evolving Digital Circuits using Multi Expression Programming, NASA/DoD Conference on Evolvable Hardware, 24-26 June, Seattle, Edited by R. Zebulum, D. Gwaltney, G. Horbny, D. Keymeulen, J. Lohn, A. Stoica, pages 87-90, IEEE Press, NJ, 2004.

13. Grosan C., Oltean Mihai, Improving the Performance of Evolutionary Algorithms for the Multiobjective 0/1 Knapsack Problem Using Epsilon-Dominance, International Conference on Computational Sciences, ICCS'04, Edited by M. Bubak, G. D. van Albada, P. Sloot, and J. Dongarra, Vol II, pp. 674-677, 6-9 June, Krakow, Poland, 2004.

2003

1. Oltean Mihai, Grosan C., A Comparison of Several Linear Genetic Programming Techniques, Complex-Systems, Vol. 14, Nr. 4, pp. 285-313, 2003.

2. Oltean Mihai, Evolving Evolutionary Algorithms for Function Optimization, Proceedings of the 5^th International Workshop on Frontiers in Evolutionary Algorithms, The 7^th Joint Conference on Information Sciences, September 26-30, 2003, Research Triangle Park, North Carolina, Edited by Ken Chen (et. al), pp. 295-298, 2003.

3. Oltean Mihai, Solving Even-Parity Problems using Multi Expression Programming, Proceedings of the 5^th International Workshop on Frontiers in Evolutionary Algorithms, The 7^th Joint Conference on Information Sciences, September 26-30, 2003, Research Triangle Park, North Carolina, Edited by Ken Chen (et. al), pp. 315-318, 2003.

4. Oltean Mihai, Grosan C., Evolving Evolutionary Algorithms using Multi Expression Programming, The 7^th European Conference on Artificial Life, September 14-17, 2003, Dortmund, Edited by W. Banzhaf (et al),  LNAI 2801, pp. 651-658, Springer-Verlag, Berlin, 2003.

5. Oltean Mihai, Grosan C., Solving Classification Problems using Infix Form Genetic Programming, The 5^th International Symposium on Intelligent Data Analysis, August 28-30, 2003, Berlin, Edited by M. Berthold (et al), LNCS 2810, pp. 242-252, Springer-Verlag, Berlin, 2003.

6. Grosan C., Oltean Mihai, Adaptive Representation for Single Objective Optimization, First Balkan Conference on Informatics, 17-20 November 2003, Thessalonica, Greece, edited by Y. Manoulopoulus (et al), pp. 345-355.
    

1998-2002

1. D. Dumitrescu, Crina Groşan and Mihai Oltean. A New Evolutionary Approach for Multiobjective Optimization, Studia Universitas Babeş-Bolyai, Informatica, Volume XLV, No. 1, pp. 51-68, 2000,

2. D. Dumitrescu, M. Oltean, An Evolutionary Algorithm for Theorem Proving in Propositional Logic, Studia, seria Informatica, Vol XLIV, Nr. 2, pp. 87-98, 1999. Babes-Bolyai University, Cluj-Napoca,

3. D. Tatar, M. Oltean, DNA Theorem Proving, Studia, 1999, seria Informatica, vol. XLIV, no 2, pp. 62-71, Babes-Bolyai University, Cluj-Napoca,

4. D. Dumitrescu, M. Oltean, Theorem proving using Resolution, Proceeding of the Joint Conference on Mathematics and Computer Science, Oradea 2001,

5. D. Dumitrescu, C. Grosan, M. Oltean, Genetic Chromodynamics for multimodal and multiobjective optimization, Proceeding of the Joint Conference on Mathematics and Computer Science, Oradea 2001,

6. M. Oltean, Prime numbers and divisibility, Ginfo, Nr 2, Computer Libris Agora, Cluj-Napoca.1999, (in romanian)

7. M. Oltean, Dynamic programming in NP-Complete problems, Ginfo, Nr 2, Computer Libris Agora, Cluj-Napoca, 2000, (in romanian)

8. M. Oltean, Word97 macros, Ginfo, Nr 8, Computer Libris Agora, Cluj-Napoca, 1999, (in romanian)

9. M. Oltean, Dirichlet’s box principles, Ginfo, Nr 6, Computer Libris Agora, Cluj-Napoca, 1999, (in romanian)

10. M. Oltean, Games programming using DirectX, Ginfo, Nr 3, Computer Libris Agora, Cluj-Napoca, 1999, (in romanian)

11. M. Oltean, C. Grosan, Ordered configurations, Ginfo, Nr 4, Computer Libris Agora, Cluj-Napoca, 2001, (in romanian)

12. M. Oltean, Crina Groşan, Evolutionary algorithms, Ginfo Nr 8, Computer Libris Agora, Cluj-Napoca, 2001 (in romanian).

13. D. Dumitrescu, C. Grosan, M Oltean, Simple Multiobjective Evolutionary Algorithm, Seminar on Computer Science, Babes- Bolyai University, Cluj-Napoca, 2001, pp. 1-12

14. D. Dumitrescu, Crina Grosan and Mihai Oltean, Genetic Chromodynamics for Obtaining Continuous Representation of Pareto Regions, Studia Universitas Babes--Bolyai, Informatica, Volume XLVI, No. 1, pp. 15-30, 2001 

15. M. Oltean, C. Groşan, Expressions modeling by using evolutionary techniques, Ginfo Nr 2, Computer Libris Agora, Cluj-Napoca, 2002,

16. D. Dumitrescu, Crina Groşan, Mihai Oltean, A new evolutionary adaptive representation paradigm, Studia Universitas Babes-Bolyai, Informatica, Volume XLVI, No. 2, pp. 19-28, 2001  .

List of Abstracts:

Oltean Mihai, A light-based device for solving the Hamiltonian path problem, Unconventional Computing, Springer-Verlag, 2006 (accepted).

In this paper we suggest the use of light for performing useful computations. Namely, we propose a special device which uses light rays for solving the Hamiltonian path problem on a directed graph. The device has a graph-like representation and the light is traversing it following the routes given by the connections between nodes. In each node the rays are uniquely marked so that they can be easily identified. At the destination node we will search only for particular rays that have passed only once through each node. We show that the proposed device can solve small and medium instances of the problem in reasonable time.
 

Oltean Mihai, Evolving Evolutionary Algorithms with Patterns, Soft Computing, Springer-Verlag (accepted), 2006

A new model for evolving Evolutionary Algorithms (EAs) is proposed in this paper. The model is based on the Multi Expression Programming (MEP) technique. Each MEP chromosome encodes an evolutionary pattern which is repeatedly used for generating the individuals of a new generation. The evolved pattern is embedded into a standard evolutionary scheme which is used for solving a particular problem. Several evolutionary algorithms for function optimization are evolved by using the considered model. The evolved evolutionary algorithms are compared with a human-designed Genetic Algorithm. Numerical experiments show that the evolved evolutionary algorithms can compete with standard approaches for several well-known benchmarking problems.
 

Oltean Mihai, Switchable Glass: A possible medium for Evolvable Hardware, NASA conference on Adaptive Hardware Systems, pp 81-87, IEEE CS Press, 2006.

The possibility of using switchable glass (also called smart windows) technology for Evolvable Hardware tasks is suggested in this paper. Switchable glass technology basically means controlling the transmission of light through windows by using electrical power. By applying a variable voltage to the window we can continuously vary the amount of transmitted light. Three existing technologies are reviewed in this paper: Electrochromic Devices, Suspended Particle Devices and Liquid Crystal Devices. An Evolvable Hardware application for a light-based device is described. The proposed device can be used for solving an entire class of problems, instead of one problem only as in the case of other dedicated hardware.
 

Oltean Mihai, Evolving Evolutionary Algorithms using Linear Genetic Programming, Evolutionary Computation, MIT Press, Vol. 13, Issue 3, pp. 387-410, 2005.

A new model for evolving Evolutionary Algorithms is proposed in this paper. The model is based on the Linear Genetic Programming (LGP) technique. Every LGP chromosome encodes an EA which is used for solving a particular problem. Several Evolutionary Algorithms for function optimization, the Traveling Salesman Problem and the Quadratic Assignment Problem are evolved by using the considered model. Numerical experiments show that the evolved Evolutionary Algorithms perform similarly and sometimes even better than standard approaches for several well-known benchmarking problems.
 

Oltean Mihai, Searching for a Practical Evidence for the No Free Lunch Theorems, BioInspired Approaches to Advanced Information Technology, BioADIT'04, Lausanne, Switzerland, 29-31 January, edited by A. Ijspeert (et al), LNCS 3141, pp. 472-483, Springer-Verlag, Berlin, 2004.

According to the No Free Lunch (NFL) theorems all blackbox algorithms perform equally well when compared over the entire set of optimization problems. An important problem related to NFL is finding a test problem for which a given algorithm is better than another given algorithm. Of high interest is finding a function for which Random Search is better than another standard evolutionary algorithm. In this paper we propose an evolutionary approach for solving this problem: we will evolve test functions for which a given algorithm A is better than another given algorithm B. Two ways for representing the evolved functions are employed: as GP trees and as binary strings. Several numerical experiments involving NFL-style Evolutionary Algorithms for function optimization are performed. The results show the effectiveness of the proposed approach. Several test functions for which Random Search performs better than all other considered algorithms have been evolved.