His primary areas of investigation include Evolutionary algorithm, Artificial intelligence, Complex network, Mathematical optimization and Genetic programming. His Evolutionary algorithm research incorporates themes from Theoretical computer science, Lattice, Evolutionary computation and Algorithm, Cellular automaton. As part of his studies on Theoretical computer science, he often connects relevant subjects like Random number generation.
Many of his studies involve connections with topics such as Machine learning and Artificial intelligence. He has researched Complex network in several fields, including Fitness landscape, Evolutionary stability, Social dilemma and Transient. His work on Genetic representation as part of general Genetic programming research is often related to Test case, thus linking different fields of science.
His main research concerns Theoretical computer science, Genetic programming, Artificial intelligence, Cellular automaton and Mathematical optimization. His Theoretical computer science research includes themes of Evolutionary algorithm, Random graph and Complex network. He has included themes like Evolutionary computation and Selection in his Evolutionary algorithm study.
His research on Genetic programming also deals with topics like
His scientific interests lie mostly in Theoretical computer science, Local optimum, Complex network, Dilemma and Metaheuristic. His work carried out in the field of Theoretical computer science brings together such families of science as Evolutionary algorithm, Network model, Artificial intelligence and Random graph. The Artificial intelligence study combines topics in areas such as Path and Machine learning.
His Local optimum research includes elements of Fitness landscape, Quadratic assignment problem, Local optima networks and Local search. Marco Tomassini interconnects Mathematical economics and Microeconomics in the investigation of issues within Dilemma. His Metaheuristic study integrates concerns from other disciplines, such as Computational complexity theory and Nest.
Dilemma, Mathematical optimization, Game theory, Local optimum and Theoretical computer science are his primary areas of study. His research integrates issues of Mathematical economics, Microeconomics and Simulation in his study of Dilemma. His biological study spans a wide range of topics, including Space, Landscape model and Flow shop scheduling.
His studies deal with areas such as Fitness landscape, Local optima networks, Operator and Problem difficulty as well as Local optimum. His Fitness landscape research is multidisciplinary, incorporating perspectives in Quadratic assignment problem and Network model, Local search, Artificial intelligence. His Theoretical computer science study incorporates themes from Coordination game and Random graph.
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Parallelism and evolutionary algorithms
E. Alba;M. Tomassini.
IEEE Transactions on Evolutionary Computation (2002)
A phylogenetic, ontogenetic, and epigenetic view of bio-inspired hardware systems
M. Sipper;E. Sanchez;D. Mange;M. Tomassini.
IEEE Transactions on Evolutionary Computation (1997)
Spatially structured evolutionary algorithms : artificial evolution in space and time
Soft Computing: Integrating Evolutionary, Neural, and Fuzzy Systems
Andrea Tettamanzi;Marco Tomassini.
On the generation of high-quality random numbers by two-dimensional cellular automata
M. Tomassini;M. Sipper;M. Perrenoud.
IEEE Transactions on Computers (2000)
Phylogeny, Ontogeny, and Epigenesis: Three Sources of Biological Inspiration for Softening Hardware
Eduardo Sanchez;Daniel Mange;Moshe Sipper;Marco Tomassini.
international conference on evolvable systems (1996)
Worldwide spreading of economic crisis
Antonios Garas;Panos Argyrakis;Céline Rozenblat;Marco Tomassini.
New Journal of Physics (2010)
Hawks and Doves on small-world networks.
Marco Tomassini;Leslie Luthi;Mario Giacobini.
Physical Review E (2006)
An Empirical Study of Multipopulation Genetic Programming
Francisco Fernández;Marco Tomassini;Leonardo Vanneschi.
Genetic Programming and Evolvable Machines (2003)
a Survey of Genetic Algorithms
applied reconfigurable computing (1995)
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