Jean-Louis Deneubourg

Bio: Jean-Louis Deneubourg is an academic researcher from Université libre de Bruxelles. The author has contributed to research in topics: Foraging & Population. The author has an hindex of 75, co-authored 437 publications receiving 25229 citations. Previous affiliations of Jean-Louis Deneubourg include Higher University of San Andrés & Centre national de la recherche scientifique.

Self-Organization in Biological Systems

Abstract: From the Publisher: "Broad in scope, thorough yet accessible, this book is a self-contained introduction to self-organization and complexity in biology - a field of study at the forefront of life sciences research."--BOOK JACKET.

The dynamics of collective sorting robot-like ants and ant-like robots

Abstract: A distributed sorting algorithm, inspired by how ant colonies sort their brood is presented for use by robot teams The robots move randomly, do not communicate have no hierarchical organisation, have no global representation can only perceive objects just in front of them, but can distinguish between objects of two or more types with a certain degree of error The probability that they pick up or put down an object is modulated as a function of how many of the same objects they have met in the recent past This generates a positive feed-back that is sufficient to coordinate the robots' activity, resulting in their sorting the objects into common clusters While less efficient than a hierarchically controlled sorting, this decentralised organisation offers the advantages of simplicity, flexibility and robustness

The self-organizing exploratory pattern of the argentine ant

Abstract: Workers of the Argentine ant, Iridomyrmex humilis,start to explore a chemically unmarked territory randomly. As the exploratory front advances, other explorers are recruited and a trail extends from it to the nest. Whereas recruitment trails are generally constructed between two points, these exploratory trails have no fixed destination, and strongly resemble the foraging patterns of army ants. A minimal model shows how the exploratory pattern may be generated by the individual workers' simple trail-laying and -following behavior, illustrating how complex collective structures in insect colonies may be based on self-organization.

Self-organized Shortcuts in the Argentine Ant

Abstract: Les fourmis I. humilis choisissent le chemin le plus court pour aller de la colonie au lieu d'approvisionnement

Collective patterns and decision-making

Abstract: Autocatalytic interactions between the members of an animal group or society, and particularly chemically or visually mediated allelomimesis, can be an important factor in the organisation of their collective activity. Furthermore, the interactions between the individuals and the environment allow different collective patterns and decisions to appear under different conditions, with the same individual behaviour. While most clearly demonstrable in social insects, these principles are fundamental to schools of fishes, flocks of birds, groups of mammals, and many other social aggregates. The analysis of collective behaviour in these terms implies detailed observation of both individual and collective behaviour, combined with mathematical modelling to link the two.

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

Genetic Programming: On the Programming of Computers by Means of Natural Selection

Abstract: Background on genetic algorithms, LISP, and genetic programming hierarchical problem-solving introduction to automatically-defined functions - the two-boxes problem problems that straddle the breakeven point for computational effort Boolean parity functions determining the architecture of the program the lawnmower problem the bumblebee problem the increasing benefits of ADFs as problems are scaled up finding an impulse response function artificial ant on the San Mateo trail obstacle-avoiding robot the minesweeper problem automatic discovery of detectors for letter recognition flushes and four-of-a-kinds in a pinochle deck introduction to biochemistry and molecular biology prediction of transmembrane domains in proteins prediction of omega loops in proteins lookahead version of the transmembrane problem evolutionary selection of the architecture of the program evolution of primitives and sufficiency evolutionary selection of terminals evolution of closure simultaneous evolution of architecture, primitive functions, terminals, sufficiency, and closure the role of representation and the lens effect Appendices: list of special symbols list of special functions list of type fonts default parameters computer implementation annotated bibliography of genetic programming electronic mailing list and public repository

Ant system: optimization by a colony of cooperating agents

Abstract: An analogy with the way ant colonies function has suggested the definition of a new computational paradigm, which we call ant system (AS). We propose it as a viable new approach to stochastic combinatorial optimization. The main characteristics of this model are positive feedback, distributed computation, and the use of a constructive greedy heuristic. Positive feedback accounts for rapid discovery of good solutions, distributed computation avoids premature convergence, and the greedy heuristic helps find acceptable solutions in the early stages of the search process. We apply the proposed methodology to the classical traveling salesman problem (TSP), and report simulation results. We also discuss parameter selection and the early setups of the model, and compare it with tabu search and simulated annealing using TSP. To demonstrate the robustness of the approach, we show how the ant system (AS) can be applied to other optimization problems like the asymmetric traveling salesman, the quadratic assignment and the job-shop scheduling. Finally we discuss the salient characteristics-global data structure revision, distributed communication and probabilistic transitions of the AS.

Ant colony system: a cooperative learning approach to the traveling salesman problem

Abstract: This paper introduces the ant colony system (ACS), a distributed algorithm that is applied to the traveling salesman problem (TSP). In the ACS, a set of cooperating agents called ants cooperate to find good solutions to TSPs. Ants cooperate using an indirect form of communication mediated by a pheromone they deposit on the edges of the TSP graph while building solutions. We study the ACS by running experiments to understand its operation. The results show that the ACS outperforms other nature-inspired algorithms such as simulated annealing and evolutionary computation, and we conclude comparing ACS-3-opt, a version of the ACS augmented with a local search procedure, to some of the best performing algorithms for symmetric and asymmetric TSPs.