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Titlebook: Nature Inspired Cooperative Strategies for Optimization (NICSO 2011); David Alejandro Pelta,Natalio Krasnogor,Rodica Lun Book 2011 Springe

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41#
發(fā)表于 2025-3-28 17:00:46 | 只看該作者
Cooperation and Self-organized Criticality for Studying Economic Crises,vestigated and the solutions that can prevent these phenomena are searched for. A new economic model inspired by the . based on a connective structure is introduced. Interesting complex behaviour emerges from the simple interactions between the agents. A new, unconventional, cause for economic crises is identified and described.
42#
發(fā)表于 2025-3-28 21:01:40 | 只看該作者
43#
發(fā)表于 2025-3-29 02:06:33 | 只看該作者
44#
發(fā)表于 2025-3-29 03:59:06 | 只看該作者
Estimation of Distribution Algorithm for the Quay Crane Scheduling Problem, to/from) by means of the available quay cranes at a container terminal. The experimental results confirm that such algorithms are suitable for solving the QCSP and perform a wide exploration of the solution space using reduced computational times.
45#
發(fā)表于 2025-3-29 07:49:21 | 只看該作者
Enhancing the Computational Mechanics of Cellular Automata,st widely studied cellular automata computation problem, the Density Classification Task, where incorporation of the proposed method almost always pushes the search beyond the simple block-expanding rules.
46#
發(fā)表于 2025-3-29 12:44:01 | 只看該作者
47#
發(fā)表于 2025-3-29 17:40:56 | 只看該作者
An Adaptive Multiagent Strategy for Solving Combinatorial Dynamic Optimization Problems, in order to obtain a more robust behaviour. The adaptive scheme is also tested on an evolutionary algorithm (EA). Finally, both Agents and EA are compared against the recent state of the art adaptive hill-climbing memetic algorithm (AHMA).
48#
發(fā)表于 2025-3-29 22:56:17 | 只看該作者
49#
發(fā)表于 2025-3-30 01:23:09 | 只看該作者
50#
發(fā)表于 2025-3-30 05:32:14 | 只看該作者
Gravitational Swarm Approach for Graph Coloring, move as particles in the gravitational field defined by some target objects corresponding to graph node colors. Knowledge of the graph to be colored is encoded in the agents as friend-or-foe information. We discuss the convergence of the algorithm and test it over well-known benchmarking graphs, achieving good results in a reasonable time.
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