Abstract-We have developed a genetic algorithm (GA) approach to the problem of task scheduling for multiprocessor systems. Our approach requires minimal problem specific information and no problem specific operators or repair mechanisms. Key features of our system include a flexible, adaptive problem representation and an incremental fitness function. Comparison with traditional scheduling methods indicates that the GA is competitive in terms of solution quality if it has sufficient resources to perform its search. Studies in a nonstationary environment show the GA is able to automatically adapt to changing targets.

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Oxidative damage, produced by mutant Cu/Zn superoxide dismutase (SOD1), may play a role in the pathogenesis of amyotrophic lateral sclerosis (ALS), a devastating motor neuron degenerative disease. A novel approach to antioxidant therapy is the use of metalloporphyrins that catalytically scavenge a wide range of reactive oxygen and reactive nitrogen species. In this study, we examined the therapeutic potential of iron porphyrin (FeTCPP) in the G93A mutant SOD1 transgenic mouse model of ALS. We found that intraperitoneal injection of FeTCPP significantly improved motor function and extended survival in G93A mice. Similar results were seen with a second group of mice wherein treatment with FeTCPP was initiated at the onset of hindlimb weakness-roughly equivalent to the time at which treatment would begin in human patients. FeTCPP-treated mice also showed a significant reduction in levels of malondialdehyde (a marker of lipid peroxidation), in total content of protein carbonyls (a marker of protein oxidation), and increased neuronal survival in the spinal cord. These results therefore provide further evidence of oxidative damage in a mouse model of ALS, and suggest that FeTCPP could be beneficial for the treatment of ALS patients.

The majority of current genetic algorithms (GAs), while inspired by natural evolutionary systems, are seldom viewed as biologically plausible models. This is not a criticism of GAs, but rather a reflection of choices made regarding the level of abstraction at which biological mechanisms are modeled, and a reflection of the more engineering-oriented goals of the evolutionary computation community. Understanding better and reducing this gap between GAs and genetics has been a central issue in an interdisciplinary project whose goal is to build GA-based computational models of viral evolution. The result is a system called Virtual Virus (VIV). VIV incorporates a number of more biologically plausible mechanisms, including a more flexible genotype-to-phenotype mapping. In VIV the genes are independent of position, and genomes can vary in length and may contain noncoding regions, as well as duplicative or competing genes. Initial computational studies with VIV have already revealed several emergent phenomena of both biological and computational interest. In the absence of any penalty based on genome length, VIV develops individuals with long genomes and also performs more poorly (from a problem-solving viewpoint) than when a length penalty is used. With a fixed linear length penalty, genome length tends to increase dramatically in the early phases of evolution and then decrease to a level based on the mutation rate. The plateau genome length (i.e., the average length of individuals in the final population) generally increases in response to an increase in the base mutation rate. When VIV converges, there tend to be many copies of good alternative genes within the individuals. We observed many instances of switching between active and inactive genes during the entire evolutionary process. These observations support the conclusion that noncoding regions serve as scratch space in which VIV can explore alternative gene values. These results represent a positive step in understanding how GAs might exploit more of the power and flexibility of biological evolution while simultaneously providing better tools for understanding evolving biological systems.

In cooperative multi-agent systems, roles are used as a design concept when creating large systems, they are known to facilitate specialization of agents, and they can help to reduce interference in multi-robot domains. The types of tasks that the agents are asked to solve and the communicative capabilities of the agents significantly affect the way roles are used in cooperative multi-agent systems. Along with a discussion of these issues about roles in multi-agent systems, this article compares computational models of the role allocation problem, presents the notion of explicitly versus implicitly defined roles, gives a survey of the methods used to approach role allocation problems, and concludes with a list of open research questions related to roles in multi-agent systems.