By Rami N. Khushaba, Ahmed Al-Ani, Akram AlSukker, Adel Al-Jumaily (auth.), Marco Dorigo, Mauro Birattari, Christian Blum, Maurice Clerc, Thomas Stützle, Alan F. T. Winfield (eds.)
This e-book constitutes the refereed complaints of the sixth overseas Workshop on Ant Colony Optimization and Swarm Intelligence, ANTS 2008, held in Brussels, Belgium, in September 2008.
The 17 revised complete papers, 24 revised brief papers, and 10 prolonged abstracts awarded have been conscientiously reviewed and chosen from ninety one submissions. The papers hide theoretical and foundational facets of computational intelligence and comparable disciplines with exact specialise in swarm intelligence and are dedicated to behavioral types of social bugs and new algorithmic ways, empirical and theoretical study in swarm intelligence, purposes reminiscent of ant colony optimization or particle swarm optimization, and theoretical and experimental learn in swarm robotics systems.
Read or Download Ant Colony Optimization and Swarm Intelligence: 6th International Conference, ANTS 2008, Brussels, Belgium, September 22-24, 2008. Proceedings PDF
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Additional info for Ant Colony Optimization and Swarm Intelligence: 6th International Conference, ANTS 2008, Brussels, Belgium, September 22-24, 2008. Proceedings
Technical Report LSI-08-13, LSI, Univeristat Polit`ecnica de Catalunya (2008) Ant Colony Optimization for Genome-Wide Genetic Analysis Casey S. Greene, Bill C. White, and Jason H. edu Abstract. In human genetics it is now feasible to measure large numbers of DNA sequence variations across the human genome. Given current knowledge about biological networks and disease processes it seems likely that disease risk can best be modeled by interactions between biological components, which can be examined as interacting DNA sequence variations.
2 Pheromone Updating with Expert Knowledge We have discovered in our work with genetic programming that expert knowledge is critical for machine learning algorithms to be successful with this problem . Here we apply principles discovered in our genetic programming work to the ACO arena and structured our pheromone updating rule such that expert knowledge is provided within the pheromone update rule. The pheromone is updated for each SNP, a, according to the following function after the ith update: τa,i+1 = τa,i ρ + Δτa,i (1) Δτa,i is the additional pheromone contributed by ants during this update cycle and ρ is the evaporation factor.
The iteration-best solution), if Po (T ib ) < Po (T rb ) and Po (T ib ) < Po (T bs ) (respectively, Pd (T ib ) < Pd (T rb ) and Pd (T ib ) < Pd (T bs ) in the case of directional antennas). ApplyPheromoneUpdate(cf ,bs update,T ,T ib ,T rb ,T bs ): Our ACO algorithm may use three diﬀerent solutions for updating the pheromone values: (i) the iteration-best solution T ib , (ii) the restart-best solution T rb and, (iii) the bestso-far solution T bs . Their inﬂuence depends on the convergence factor cf , which provides an estimate about the state of convergence of the system.
Ant Colony Optimization and Swarm Intelligence: 6th International Conference, ANTS 2008, Brussels, Belgium, September 22-24, 2008. Proceedings by Rami N. Khushaba, Ahmed Al-Ani, Akram AlSukker, Adel Al-Jumaily (auth.), Marco Dorigo, Mauro Birattari, Christian Blum, Maurice Clerc, Thomas Stützle, Alan F. T. Winfield (eds.)