25,179 research outputs found
Self-loops favour diversification and asymmetric transitions between attractors in boolean network models
No full textThe process of cell differentiation manifests properties such as non-uniform robustness and asymmetric transitions among cell types. In this paper we adopt Boolean networks to model cellular differentiation, where attractors (or set of attractors) in the network landscape epitomise cell types. Since changes in network topology and functions strongly impact attractor landscape characteristics, in this paper we study how self-loops influence diversified robustness and asymmetry of transitions. The purpose of this study is to identify the best configuration for a network owning these properties. Our results show that a moderate amount of self-loops make random Boolean networks more suitable to reproduce differentiation phenomena. This is a further evidence that self-loops play an important role in genetic regulatory networks
Spirito didattico e sguardo artistico : etica e poetica di Stifter nei saggi letterari e nelle recensioni pittoriche e teatrali
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Hybrid Metaheuristics: An Introduction
No full textIn many real life settings, high quality solutions to hard optimization problems such as flight scheduling or load balancing in telecommunication networks are required in a short amount of time. Due to the practical importance of optimization problems for industry and science, many algorithms to tackle them have been developed. One important class of such algorithms are metaheuristics. The field of metaheuristic research has enjoyed a considerable popularity in the last decades. In this introductory chapter we first provide a general overview on metaheuristics. Then we turn towards a new and highly successful branch of metaheuristic research, namely the hybridization of metaheuristics with algorithmic components originating from other techniques for optimization. The chapter ends with an outline of the remaining book chapters
Hybrid Metaheuristics: Preface to the proceedings of HM2005
No full textCombinatorial optimization attracted many researchers since more than three decades.
Plenty of classical hard problems have been tackled successfully with metaheuristic approaches. Several thereof are currently considered state-of-the-art methods for such problems. However, for many years the main focus of research was on the application of single metaheuristics to given problems. A tendency to compare different metaheuristics against each other could be observed, and sometimes this competition led to thinking in stereotypes in the
research communities.
In recent years, it has become evident that the concentration on a sole metaheuristic is rather restrictive, when focusing on the improvement of heuristic techniques to tackle both academic and practical optimization problems. A skilled combination of concepts stemming from different metaheuristics can provide a more efficient behavior and a higher flexibility. Also the hybridization of metaheuristics with other techniques known from classical artificial intelligence areas can be very fruitful. Further, the incorporation of typical operations research techniques can be very beneficial. Combinations of metaheuristic components with components from other metaheuristics or optimization strategies from artificial intelligence or operations research are called hybrid metaheuristics.
The design and implementation of hybrid metaheuristics rises problems going beyond questions about the composition of a single metaheuristic. The proper interaction of
different algorithm components must usually be based on a careful analysis of the single components. Choice and tuning of parameters is more important for the quality of the algorithms than before. Different concepts of interaction at low-level and at high-level are studied. As a result, the design of experiments and the proper statistical evaluation are in a more exposed position than before.
We believe that the combination of elements coming from different metaheuristics, and from classical methods from both artificial intelligence and operations research, bears great chances to become one of the main tracks of research in applied artificial intelligence. It seems to be a promising and rewarding alternative to the still existing mutual contempt between the fields of exact methods and approximate techniques, and also to the competition between the different schools of metaheuristics, which sometimes focused more on a proof of concept than on good general results.
Still, we have to realize that research on hybrid metaheuristics is in main parts based on experimental methods, thus being probably more related to natural sciences than to computer science. It can be stated that both the design and the evaluation of experiments have still not reached the standard as they have in physics or chemistry
for example. The validity of analyses of experimental work on algorithms is a key aspect in hybrid metaheuristics, and the attention of researchers to this aspect seems to be important for the future of the field
The impact of self-loops in random boolean network dynamics: A simulation analysis
No full textRandom Boolean Networks (RBNs) are a popular and successful model of gene regulatory networks, especially for analysing emergent properties of cell dynamics. Since completely random networks are unrealistic, some work has been done to extend the original model with structural and functional properties observed in biological networks. Among recurring motifs identified by experimental studies, auto-regulation seems to play a significant role in gene regulatory networks. In this paper we present a model of auto-regulatory mechanisms by introducing self-loops in RBNs. Experiments are performed to analyse the impact of self-loops in the RBNs asymptotic behaviour. Results show that the number of attractors increases with the amount of self-loops, while their robustness and stability decrease
Evolving critical boolean networks
No full textRandom Boolean networks are a widely acknowledged model for cell dynamics. Previous studies have shown the possibility of achieving Boolean networks with given characteristics by means of evolutionary techniques. In this work we make a further step towards more biologically plausible models by aiming at evolving networks with a given fraction of active nodes along the attractors, while constraining the evolutionary process to move across critical networks. Results show that this path along criticality does not impede to climb the mount of improbable, yet biologically realistic requirements
Preface to Hybrid Metaheuristics - 6th International Workshop, HM 2009
No full textThe International Workshop on Hybrid Metaheuristics was established with the aim of providing researchers and scholars with a forum for discussing new ideas and research on metaheuristics and their integration with techniques typical of other fields. The papers accepted for the sixth workshop confirm that such a combination is indeed effective and that several research areas can be put together. Slowly but surely, this process has been promoting productive dialogue among researchers with different expertise and eroding barriers between research areas.
The papers in this volume give a representative sample of current research in hybrid metaheuristics. It is worth emphasizing that this year, a large number of papers demonstrated how metaheuristics can be integrated with integer linear programming and other operations research techniques. Constraint programming is also featured, which is a notable representative of artificial intelligence solving methods. Most of these papers are not only a proof of concept – which can be valuable by itself – but also show that the hybrid techniques presented tackle difficult and relevant problems
Criticality and Parallelism in GSAT
No full textIn this work we show some empirical results on the parallelization of GSAT. We subdivided the set of variables in τ equal subsets and we applied GSAT in parallel on each subset. We observed the existence of an optimum degree of parallelism (τopt) for which the best performance, in terms of efficiency (time and number of iterations) and effectiveness (fraction of solved instances) is obtained. Moreover, we found that τopt is strictly correlated to the connectivity parameter (q)
A third transition in science?
No full textSince Newton, classical and quantum physics depend upon the "Newtonian Paradigm". The relevant variables of the system are identified. For example, we identify the position and momentum of classical particles. Laws of motion in differential form connecting the variables are formulated. An example is Newton's three Laws of Motion. The boundary conditions creating the phase space of all possible values of the variables are defined. Then, given any initial condition, the differential equations of motion are integrated to yield an entailed trajectory in the pre-stated phase space. It is fundamental to the Newtonian Paradigm that the set of possibilities that constitute the phase space is always definable and fixed ahead of time.
This fails for the diachronic evolution of ever-new adaptations in any biosphere. Living cells achieve Constraint Closure and construct themselves. Thus, living cells, evolving via heritable variation and Natural selection, adaptively construct new-in-the-universe possibilities. We can neither define nor deduce the evolving phase space: We can use no mathematics based on Set Theory to do so. We cannot write or solve differential equations for the diachronic evolution of ever-new adaptations in a biosphere.
Evolving biospheres are outside the Newtonian Paradigm. There can be no Theory of Everything that entails all that comes to exist.
We face a third major transition in science beyond the Pythagorean dream that ``All is Number'' echoed by Newtonian physics.
However, we begin to understand the emergent creativity of an evolving biosphere: Emergence is not engineering
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