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48
Adaptive Computational Chemotaxis in Bacterial Foraging Optimization: An Analysis
 IEEE Computer Society Press, ISBN 0769531091
, 2008
"... Some researchers have illustrated how individual and groups of bacteria forage for nutrients and to model it as a distributed optimization process, which is called the Bacterial Foraging Optimization (BFOA). One of the major driving forces of BFOA is the chemotactic movement of a virtual bacterium, ..."
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Some researchers have illustrated how individual and groups of bacteria forage for nutrients and to model it as a distributed optimization process, which is called the Bacterial Foraging Optimization (BFOA). One of the major driving forces of BFOA is the chemotactic movement of a virtual bacterium, which models a trial solution of the optimization problem. In this article, we analyze the chemotactic step of a one dimensional BFOA in the light of the classical Gradient Descent Algorithm (GDA). Our analysis points out that chemotaxis employed in BFOA may result in sustained oscillation, especially for a flat fitness landscape, when a bacterium cell is very near to the optima. To accelerate the convergence speed near optima we have made the chemotactic step size C adaptive. Computer simulations over several numerical benchmarks indicate that BFOA with the new chemotactic operation shows better convergence behavior as compared to the classical BFOA.
Satisfiability of Nonlinear (Ir)rational Arithmetic
 16th International Conference on Logic for Programming, Artificial Intelligence and Reasoning, LPAR’10
, 2010
"... Abstract. We present a novel way for reasoning about (possibly ir)rational quantifierfree nonlinear arithmetic by a reduction to SAT/SMT. The approach is incomplete and dedicated to satisfiable instances only but is able to produce models for satisfiable problems quickly. These characteristics s ..."
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Abstract. We present a novel way for reasoning about (possibly ir)rational quantifierfree nonlinear arithmetic by a reduction to SAT/SMT. The approach is incomplete and dedicated to satisfiable instances only but is able to produce models for satisfiable problems quickly. These characteristics suffice for applications such as termination analysis of rewrite systems. Our prototype implementation, called MiniSmt, is made freely available. Extensive experiments show that it outperforms current SMT solvers especially on rational and irrational domains.
Embodied artificial evolution Artificial evolutionary systems in the 21st Century
"... Ó The Author(s) 2012. This article is published with open access at Springerlink.com Abstract Evolution is one of the major omnipresent powers in the universe that has been studied for about two centuries. Recent scientific and technical developments make it possible to make the transition from pass ..."
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Ó The Author(s) 2012. This article is published with open access at Springerlink.com Abstract Evolution is one of the major omnipresent powers in the universe that has been studied for about two centuries. Recent scientific and technical developments make it possible to make the transition from passively understanding to actively using evolutionary processes. Today this is possible in Evolutionary Computing, where human experimenters can design and manipulate all components of evolutionary processes in digital spaces. We argue that in the near future it will be possible to implement artificial evolutionary processes outside such imaginary spaces and make them physically embodied. In other words, we envision the ‘‘Evolution of Things’’, rather than just the evolution of digital objects, leading to a new field of Embodied Artificial Evolution (EAE). The main objective of this paper is to present a unifying vision in order to aid the development of this high potential research area. To this end, we introduce the notion of EAE, discuss a few examples and applications, and elaborate on the expected benefits as well as the grand challenges this developing field will have to address.
Evolution In Materio
, 2005
"... This thesis describes a method to program materials directly to perform a computation. The work demonstrates that an evolutionary algorithm can exploit the physical properties of materials such as liquid crystal, enabling them to perform computation. The thesis demonstrates the approach applied to s ..."
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This thesis describes a method to program materials directly to perform a computation. The work demonstrates that an evolutionary algorithm can exploit the physical properties of materials such as liquid crystal, enabling them to perform computation. The thesis demonstrates the approach applied to several different problems including signal processing, control and digital logic. In addition to demonstrating the technique on real liquid crystal, simulations are used to show the applicability to cellular automata and a kind of neural network. The thesis also argues that the developed technique may also be suitable for programming systems, such as, bacterial consortia to perform computations.
An Improved Harmony Search Algorithm with Differential Mutation Operator
, 2009
"... Harmony Search (HS) is a recently developed stochastic algorithm which imitates the music improvisation process. In this process, the musicians improvise their instrument pitches searching for the perfect state of harmony. Practical experiences, however, suggest that the algorithm suffers from the ..."
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Harmony Search (HS) is a recently developed stochastic algorithm which imitates the music improvisation process. In this process, the musicians improvise their instrument pitches searching for the perfect state of harmony. Practical experiences, however, suggest that the algorithm suffers from the problems of slow and/or premature convergence over multimodal and rough fitness landscapes. This paper presents an attempt to improve the search performance of HS by hybridizing it with Differential Evolution (DE) algorithm. The performance of the resulting hybrid algorithm has been compared with classical HS, the global best HS, and a very popular variant of DE over a testsuite of six well known benchmark functions and one interesting practical optimization problem. The comparison is based on the following performance indices (i) accuracy of final result, (ii) computational speed, and (iii) frequency of hitting the optima.
Dynamics of a public investment game: from nearestneighbor lattices to smallworld networks
 Advances in Artificial Economics, The Economy as a Complex Dynamic System, number 584 in Lecture Notes in Economics and Mathematical Systems, chapter 16
, 2006
"... In the fields of complex systems and multiagent systems there is an extensive list of publications on nontrivial phenomena which arise due to the interplay between microscopic (individual) rules and macroscopic (group) behavior. In the context of socioeconomic behavior, this has been thoroughly disc ..."
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In the fields of complex systems and multiagent systems there is an extensive list of publications on nontrivial phenomena which arise due to the interplay between microscopic (individual) rules and macroscopic (group) behavior. In the context of socioeconomic behavior, this has been thoroughly discussed by Durlauf [2]. Within
The Graph Crossing Number and its Variants: A Survey
"... The crossing number is a popular tool in graph drawing and visualization, but there is not really just one crossing number; there is a large family of crossing number notions of which the crossing number is the best known. We survey the rich variety of crossing number variants that have been introdu ..."
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The crossing number is a popular tool in graph drawing and visualization, but there is not really just one crossing number; there is a large family of crossing number notions of which the crossing number is the best known. We survey the rich variety of crossing number variants that have been introduced in the literature for purposes that range from studying the theoretical underpinnings of the crossing number to crossing minimization for visualization problems. 1 So, Which Crossing Number is it? The crossing number, cr(G), of a graph G is the smallest number of crossings required in any drawing of G. Or is it? According to a popular introductory textbook on combinatorics [320, page 40] the crossing number of a graph is “the minimum number of pairs of crossing edges in a depiction of G”. So, which one is it? Is there even a difference? To start with the second question, the easy answer is: yes, obviously there is a difference, the difference between counting all crossings and counting pairs of edges that cross. But maybe these different ways of counting don’t make a difference and always come out
AnMDL approach to efficiently discover communities in bipartite network
 in Database Systems for Advanced Applications
"... Abstract. Bipartite network is a branch of complex network. It is widely used in many applications such as social network analysis, collaborative filtering and information retrieval. Partitioning a bipartite network into smaller modules helps to get insight of the structure of the bipartite network. ..."
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Abstract. Bipartite network is a branch of complex network. It is widely used in many applications such as social network analysis, collaborative filtering and information retrieval. Partitioning a bipartite network into smaller modules helps to get insight of the structure of the bipartite network. The main contributions of this paper include: (1) proposing an MDL 21 criterion for identifying a good partition of a bipartite network. (2) presenting a greedy algorithm based on combination theory, named as MDLgreedy, to approach the optimal partition of a bipartite network. The greedy algorithm automatically searches for the number of partitions, and requires no user intervention. (3) conducting experiments on synthetic datasets and the southern women dataset. The results show that our method generates higher quality results than the stateofart methods CrossAssociation and Informationtheoretic coclustering. Experiment results also show the good scalability of the proposed algorithm. The highest improvement could be up to about 14 % for the precision, 40 % for the ratio and 70 % for the running time.
MultiAgent Learning for
 Control of Internet Traffic Routing”, Learning Systems for Control, IEE Seminar
, 2000
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Single Parent Genetic Programming
"... The most controversial part of genetic programming is its highly disruptive and potentially innovative subtree crossover operator. The clearest problem with the crossover operator is its potential to induce defensive metaselection for large parse trees, a process usually termed “bloat. ” Single pare ..."
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The most controversial part of genetic programming is its highly disruptive and potentially innovative subtree crossover operator. The clearest problem with the crossover operator is its potential to induce defensive metaselection for large parse trees, a process usually termed “bloat. ” Single parent genetic programming is a form of genetic programming in which bloat is reduced by doing subtree crossover with a fixed population of ancestor trees. Analysis of mean tree size growth demonstrates that this fixed and limited set of crossover partners provides implicit, automatic control on tree size in the evolving population, reducing the need for additionally disruptive trimming of large trees. The choice of ancestor trees can also incorporate expert knowledge into the genetic programming system. The system is tested on four problems: plusonerecallstore (PORS), odd parity, plustimeshalf (PTH) and a bioinformatic model fitting problem (NIPs). The effectiveness of the technique varies with the problem and choice of ancestor set. At the extremes, improvements in time to solution in excess of 4700fold were observed for the PORS problem, and no significant improvements for the PTH problem were observed. I.