

2009

Oltean Mihai, Muntean
Oana,
Solving the
subset sum problem with a lightbased computer,
Natural Computing, SpringerVerlag,
Vol 8, Issue 2, pp.
321331, 2009 [pdf]
[website] [Springer]

Oltean Mihai,
Lightbased string
matching, Natural Computing, SpringerVerlag,
Vol 8. Issue 1, pp. 121132 [pdf] [Springer]

Oana Muntean, Mihai Oltean,
Using
light for solving the unbounded subsetsum problem,
International Journal of Innovative Computing, Information and
Control,
Vol 5, Issue 8, 21592167, 2009 [pdf]

Oana Muntean, Mihai Oltean,
Deciding
whether a linear Diophantine equation has solutions by using a
lightbased device, Journal of Optoelectronics and
Advanced Materials, Vol. 11, Issue 11, pp. 17281734, 2009 [pdf]

Mihai Oltean, Oana Muntean, Evolutionary
design of graphbased structures for optical computing,
in proceedings of the second workshop on Optical SuperComputing, OSC
2009, LNCS 5882, pp. 5669, SpringerVerlag, 2009.

Mihai Oltean, Laura Diosan,
An autonomous GPbased system for regression and classification
problems,
Applied Soft Computing, Vol. 9, Issue 1, pp. 4960, 2009

Diosan Laura, Mihai Oltean,
Evolutionary design of Evolutionary Algorithms,
Genetic Programming and Evolvable Machines, Springer, Vol 10, Issue
3, pp. 263306, 2009

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. 197238, 2009.
2008

Oltean Mihai,
Solving the
Hamiltonian path problem with a lightbased computer,
Natural Computing, SpringerVerlag, Vol 6, Issue 1, pp.
5770, 2008 [pdf]
[website] [Springer]

Oltean Mihai,
Muntean Oana,
Exact Cover with Light, New Generation Computing,
SpringerVerlag, Vol. 26, Issue 4, pp. 327344, 2008
[pdf]
[website]

Mihai Oltean, Oana Muntean, Solving
NPComplete Problems with Delayed Signals: An Overview of Current
Research Directions, in proceedings of 1^{st} international
workshop on Optical SuperComputing, LNCS 5172, pp. 115128,
SpringerVerlag, 2008
2007

Muntean, Oana;
Oltean, Mihai, Processing Bank Checks with Genetic
Programming and Histograms, Bioinspired, Learning, and
Intelligent Systems for Security, BLISS 2007, pp. 102105, IEEE
Press, 2007

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.
869874, IEEE CS Press [abstract], [html]

Muntean O., Dioşan, L.,
Oltean, Mihai,
Solving the evennparity problems using Best Sub Tree Genetic
Programming,
Adaptive Hardware Systems 2007, pp. 511518, IEEE Press, 2007  [abstract],
[html]

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

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

Dioşan, L.,
Oltean, Mihai,
Evolving Evolutionary Algorithms using Evolutionary Algorithms,
GECCO 2007, 24422449, ACM Press  [abstract],
[html]

Dioşan, L., Oltean, Mihai,
Observing the swarm behavior during its evolutionary design,
GECCO
2007, 26672674, ACM Press  [abstract], [html]

Oltean
Mihai,
Evolving
Evolutionary Algorithms with Patterns,
Soft Computing,
SpringerVerlag, Vol. 11, Issue 6, pp. 503518, 2007 [abstract]
[pdf]

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. 227248 [pdf]
[Taylor
and Francis]

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. 87110, WorldScientific.

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, SpringerVerlag, pp.
220229, 2007. [pdf]
(best paper award)

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, SpringerVerlag, pp. 218227, 2007.
2006

Oltean Mihai,
A lightbased device for solving the
Hamiltonian path problem, Unconventional Computing,
Calude C. (et al.)(Eds), LNCS 4135, pp. 217227, SpringerVerlag, 2006 [abstract],
[ppt], [html]
[Springer]

Oltean Mihai,
Switchable Glass: A possible medium for Evolvable Hardware,
NASA conference on Adaptive Hardware Systems, Stoica A., et al. (Eds), pp 8187, IEEE CS
Press, 2006 [abstract].

Dioşan, L., Oltean,
Mihai,
Evolving crossover operators for function
optimization, European Conference on Genetic Programming,
LNCS, SpringerVerlag, pp. 97108, 2006 (best
paper nominee)

Dioşan,
L., Oltean
Mihai.,
Evolving the structure of the Particle Swarm
Optimization algorithms, European
Conference on Evolutionary Computation in Combinatorial Optimization,
LNCS, SpringerVerlag, pp. 2536, 2006.
2005

Oltean
Mihai,
Evolving Evolutionary Algorithms using Linear
Genetic Programming, Evolutionary Computation, MIT Press,
Vol. 13, Issue 3, pp. 387410, 2005. [abstract]
[pdf]

Grosan C., Oltean
Mihai, Adaptive Representation for Single
Objective Optimization, Soft Computing, SpringerVerlag, 9(8):
594605, 2005.

Oltean
Mihai,
Evolving reversible circuits for the evenparity 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. 225234, SpringerVerlag,
Berlin, 2005.

Dumitrescu D., Grosan
C., Oltean
Mihai,
Evolving Continuous
Pareto Regions, Evolutionary Computation Based MultiCriteria
Optimization: Theoretical Advances and Applications, edited by A.
Abraham, L. Jain and R. Goldberg, SpringerVerlag, London,
pp. 167199, 2005.
2004

Oltean
Mihai, A Practical
Evidence for the No Free Lunch Theorems, BioInspired Approaches to
Advanced Information Technology, BioADIT'04, Lausanne, Switzerland,
2931 January, edited by A. Ijspeert (et al), pp. 382388, 2004.

Oltean
Mihai,
Searching for a
Practical Evidence for the No Free Lunch Theorems, BioInspired
Approaches to Advanced Information Technology, BioADIT'04, Lausanne,
Switzerland, 2931 January, edited by A. Ijspeert (et al), LNCS 3141,
pp. 472483, SpringerVerlag, Berlin, 2004.
[abstract]

Oltean
Mihai (et al.),
Evolving Digital Circuits for the Knapsack
Problem, International Conference on Computational Sciences,
EHARD Workshop, 69 June, Krakow, Poland, Edited by M. Bubak, G. D. van Albada, P. Sloot, and J. Dongarra, Vol. III, pp. 12571264, SpringerVerlag, Berlin, 2004.

Oltean, M.,
Solving
EvenParity Problems using Traceless Genetic Programming, IEEE
Congress on Evolutionary Computation, Portland, 1923 June, edited by G.
Greenwood (et. al), pages 18131819, IEEE Press, 2004.

Oltean
Mihai (et al.),
Encoding Multiple Solutions in a Linear GP
Chromosome, International Conference on Computational Sciences,
EHARD Workshop, 69 June, Krakow, Poland, Edited by M. Bubak, G. D. van Albada, P. Sloot, and J. Dongarra, Vol III, pp. 12811288, SpringerVerlag, Berlin, 2004.

Oltean
Mihai,
Evolving Winning
Strategies for Nimlike Games, World Computer Congress, Student
Forum, 2629 August,
Toulouse,
France, edited by Mohamed Kaaniche, pp. 353364, Kluwer Academic
Publisher, 2004.

Oltean
Mihai, Solving
Classification Problems using Traceless Genetic Programming,
World Computer Congress, The Symposium on Professional Practice in AI,
2629 August, Toulouse, France, edited by E. MercierLaurent, J.
Debenham, pp. 403412, 2004.

Oltean
Mihai, Dumitrescu, D.,
Evolving TSP Heuristics using
Multi Expression Programming, International Conference on
Computational Sciences, ICCS'04, 69 June, Krakow, Poland, Edited by M. Bubak, G. D. van Albada,
P. Sloot, and J. Dongarra, Vol II,
pp. 670673, SpringerVerlag, Berlin, 2004.

Oltean,
Mihai, Dumitrescu, D., A
Permutation based Approach for the 2D Cutting Stock Problem, First
International Industrial Conference Bionik
2004, 2223 April, Hanover, Germany, Edited by I. Boblan, and R. Bannasch,
pp. 7380, 2004.

Oltean
Mihai,
Improving the
Search by Encoding Multiple Solutions in a Chromosome, contributed
chapter, Evolutionary Machine Design, pages 85110, Nova Science Publisher, NewYork, edited by
Nadia Nedjah (et. al).

Oltean
Mihai,
Improving Multi
Expression Programming: an Ascending
Trail from Sealevel Even3parity Problem to Alpine Even18Parity
Problem, contributed
chapter, Evolvable Machines: Theory and Practice, edited by
Nadia Nedjah (et. al), pages 229255, SpringerVerlag, Berlin, 2004.

Oltean
Mihai, Grosan C.,
Evolving
Digital Circuits using Multi Expression Programming, NASA/DoD
Conference on Evolvable Hardware, 2426 June, Seattle, Edited by R.
Zebulum, D. Gwaltney, G. Horbny, D. Keymeulen, J. Lohn, A. Stoica, pages 8790, IEEE Press, NJ, 2004.

Grosan C., Oltean
Mihai,
Improving the Performance of
Evolutionary Algorithms for the Multiobjective 0/1 Knapsack Problem
Using EpsilonDominance, International Conference on
Computational Sciences, ICCS'04, Edited by M. Bubak, G. D. van Albada,
P. Sloot, and J. Dongarra, Vol II,
pp. 674677, 69 June, Krakow, Poland, 2004.
2003

Oltean
Mihai, Grosan C., A Comparison of Several Linear
Genetic Programming Techniques, ComplexSystems, Vol. 14, Nr. 4,
pp. 285313, 2003.

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 2630, 2003,
Research Triangle Park, North Carolina, Edited by Ken Chen (et. al),
pp. 295298, 2003.

Oltean
Mihai,
Solving EvenParity 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 2630, 2003, Research Triangle Park,
North Carolina, Edited by Ken Chen (et. al), pp. 315318, 2003.

Oltean
Mihai, Grosan C.,
Evolving Evolutionary Algorithms using Multi
Expression Programming, The 7^{th}
European Conference on Artificial Life, September 1417, 2003,
Dortmund, Edited by W. Banzhaf (et al),
LNAI 2801, pp.
651658, SpringerVerlag, Berlin, 2003.

Oltean
Mihai, Grosan C.,
Solving Classification Problems using Infix Form
Genetic Programming, The 5^{th} International Symposium on Intelligent
Data Analysis, August 2830, 2003, Berlin, Edited by M. Berthold (et al),
LNCS 2810, pp. 242252, SpringerVerlag, Berlin, 2003.

Grosan C., Oltean
Mihai, Adaptive Representation for Single Objective
Optimization, First Balkan Conference on Informatics, 1720 November
2003, Thessalonica, Greece, edited by Y. Manoulopoulus (et al), pp.
345355.
19982002

D.
Dumitrescu, Crina Groşan and Mihai Oltean. A New Evolutionary Approach for Multiobjective
Optimization, Studia Universitas BabeşBolyai, Informatica, Volume
XLV, No. 1, pp. 5168, 2000,

D. Dumitrescu, M. Oltean, An
Evolutionary Algorithm for Theorem Proving in Propositional Logic,
Studia, seria Informatica, Vol
XLIV,
Nr. 2, pp. 8798, 1999. BabesBolyai
University, ClujNapoca,

D. Tatar, M. Oltean, DNA Theorem
Proving, Studia, 1999, seria Informatica, vol. XLIV,
no 2, pp. 6271, BabesBolyai
University, ClujNapoca,

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

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,

M. Oltean, Prime numbers and
divisibility, Ginfo, Nr 2, Computer Libris Agora, ClujNapoca.1999,
(in romanian)

M. Oltean, Dynamic programming in
NPComplete problems, Ginfo, Nr 2, Computer Libris Agora, ClujNapoca,
2000, (in romanian)

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

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

M. Oltean, Games programming using
DirectX, Ginfo, Nr 3, Computer Libris Agora, ClujNapoca, 1999, (in
romanian)

M. Oltean, C. Grosan, Ordered
configurations, Ginfo, Nr 4, Computer Libris Agora, ClujNapoca, 2001,
(in romanian)

M. Oltean, Crina Groşan, Evolutionary
algorithms, Ginfo Nr 8, Computer Libris Agora, ClujNapoca, 2001 (in
romanian).

D. Dumitrescu, C. Grosan, M Oltean,
Simple Multiobjective Evolutionary Algorithm, Seminar on Computer
Science, Babes Bolyai University, ClujNapoca, 2001, pp. 112

D. Dumitrescu, Crina Grosan and Mihai
Oltean, Genetic Chromodynamics for Obtaining Continuous Representation
of Pareto Regions, Studia Universitas BabesBolyai, Informatica,
Volume XLVI, No. 1, pp. 1530, 2001

M. Oltean, C. Groşan, Expressions
modeling by using evolutionary techniques, Ginfo Nr 2, Computer Libris
Agora, ClujNapoca, 2002,

D. Dumitrescu, Crina Groşan, Mihai
Oltean, A new evolutionary adaptive representation paradigm, Studia Universitas
BabesBolyai, Informatica,
Volume XLVI, No. 2, pp. 1928, 2001 .
List of
Abstracts:
Oltean Mihai,
A lightbased device for solving the Hamiltonian
path problem, Unconventional Computing, SpringerVerlag,
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 graphlike 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,
SpringerVerlag (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 humandesigned
Genetic Algorithm. Numerical experiments show that the evolved
evolutionary algorithms can compete with standard approaches for several
wellknown benchmarking problems.
Oltean Mihai,
Switchable Glass: A possible medium for Evolvable Hardware,
NASA conference on Adaptive Hardware Systems, pp 8187, 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 lightbased
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. 387410, 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 wellknown 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, 2931 January, edited by A. Ijspeert (et al), LNCS 3141,
pp. 472483, SpringerVerlag, 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 NFLstyle 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.

