This paper presents an approach to the automatic generation of electromechanical engineering designs. We apply messy genetic algorithm~GA! optimization techniques to the evolution of assemblies composed of Lego TM structures. Each design is represented as a labeled assembly graph and is evaluated based on a set of behavior and structural equations. The initial populations are generated at random, and design candidates for subsequent generations are produced by user-specified selection techniques. Crossovers are applied by using cut and splice operators at the random points of the chromosomes; random mutations are applied to modify the graph with a certain low probability. This cycle continues until a suitable design is found. The research contributions in this work include the development of a new GA encoding scheme for mechanical assemblies~Legos!, as well as the creation of selection criteria for this domain. Our eventual goal is to introduce a simulation of electromechanical devices into our evaluation functions. We believe that this research creates a foundation for future work and it will apply GA techniques to the evolution of more complex and realistic electromechanical structures.

Mobile ad-hoc networks (MANETs) are an increasingly important networking paradigm that will be the backbone of important defense and first response networks. Group decision-making is key to these environments, but is made difficult when MANETs are introduced due to network disruptions, bandwidth limitations, and host mobility patterns. Results gathered using standard group decisionmaking algorithms can become inaccurate, time-insensitive, or computationally undecidable. This paper focuses on a group decisionmaking approach using agent-based quorum sensing (ABQS) on MANETs. A mobile agent collects information (e.g., votes) from each host on a network until it can make an informed decision about global preference. This agent exploits the inherent tradeoff between efficient vote collection and result accuracy in order to provide better results, when considering survivability, hosts visited, hops made, and time spent, with only a very slight drop in correctnessbenefits that greatly outweigh costs. Experimental evidence from live MANETs demonstrates the effectiveness of this solution.

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