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An incremental genetic algorithm approach to multiprocessor scheduling
TLDR
We have developed a genetic algorithm (GA) approach to the problem of task scheduling for multiprocessor systems. Expand
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Sensor Network Optimization Using a Genetic Algorithm
TLDR
In this paper, we propose an efficient method based on genetic algorithms (GAs) to solve a sensor network optimization problem. Expand
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The Current State of Normative Agent-Based Systems
TLDR
We introduce social norms and their application to agent-based systems; identify and describe a normative process abstracted from the existing research; and discuss future directions for research in normative multiagent computing. Expand
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Evolving control for distributed micro air vehicles
We focus on the task of large area surveillance. Given an area to be surveilled and a team of micro air vehicles (MAVs) with appropriate sensors, the task is to dynamically distribute the MAVsExpand
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Putting More Genetics into Genetic Algorithms
TLDR
The majority of current genetic algorithms (GAs), while inspired by natural evolutionary systems, are seldom viewed as biologically plausible models. Expand
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Decision tree classifier for network intrusion detection with GA-based feature selection
TLDR
We use a genetic algorithm to select a subset of input features for decision tree classifiers, with a goal of increasing the detection rate and decreasing the false alarm rate in network intrusion detection. Expand
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Iron porphyrin treatment extends survival in a transgenic animal model of amyotrophic lateral sclerosis
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. AExpand
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Multi-agent role allocation: issues, approaches, and multiple perspectives
TLDR
In cooperative multi-agent systems, roles are used as a design concept when creating large systems, they are known to facilitate specialization, and they can help to reduce interference in multi-robot domains. Expand
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Empirical Studies of the Genetic Algorithm with Noncoding Segments
TLDR
The genetic algorithm (GA) is a problem-solving method that is modeled after the process of natural selection. Expand
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Empirical Observations on the Roles of Crossover and Mutation
TLDR
This paper investigates the roles of crossover and mutation by observing the actions and eeects of individual occurrences of each genetic operation within a GA run. Expand
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