Heterogeneous Multiprocessor Scheduling with Differential Evolution

The problem of scheduling a parallel program given by a Directed Acyclic Graph (DAG) of tasks is a well-studied area. We present a new approach which employs Differential Evolution to numerically optimize the priorities of tasks. Our algorithm starts with a number of acceptable solutions, results of different heuristics, and merges them to achieve better one in a small number of function evaluations. The algorithm outperforms both a number of greedy heuristics and a classical genetic algorithm on the most of the program graphs considered in our experiments

Coevolutionary Game-Theoretic Multi-Agent Systems: the Application to Mapping and Scheduling Problems

Multi-agent systems based on iterated, noncooperative N-person games with limited interaction are considered. Each player in the game has a payoff function and a set of actions. While each player acts to maximise his payoff, we are interested in the global behavior of the team of players, measured by the average payoff received by the team. To evolve a global behavior in the system, we propose two coevolutionary schemes with evaluation only local fitness functions. The first scheme we call loosely coupled genetic algorithms, and the second one loosely coupled classifier systems. We present simulation results which indicate that the global behavior in both systems evolves, and is achieved only by a local cooperation between players acting without global information about the system. The models of coevolutionary multi-agent systems are applied to develop parallel and distributed algorithms of dynamic mapping and scheduling tasks in parallel computers. Institute of Computer Science, Pol..

Heterogeneous Multiprocessor Scheduling with Differential Evolution

The problem of scheduling a parallel program given by a Directed Acyclic Graph (DAG) of tasks is a well-studied area. We present a new approach which employs Differential Evolution to numerically optimize the priorities of tasks. Our algorithm starts with a number of acceptable solutions, results of different heuristics, and merges them to achieve better one in a small number of function evaluations. The algorithm outperforms both a number of greedy heuristics and a classical genetic algorithm on the most of the program graphs considered in our experiments

EVALUATION OF STRATEGIES FOR CO-EVOLUTIONARY GENETIC ALGORITHMS: DLCGA CASE STUDY GRÉGOIRE DANOY

Dafo, a multi-agent framework dedicated to distributed coevolutionary genetic algorithms (CGAs) is used to evaluate dLCGA, a new dynamic competitive coevolutionary genetic algorithm. We compare the performance of dLCGA to other known classes of CGAs for the Inventory Control Parameter optimization problem (ICP) and in particular show how it improves the results of the static version of LCGA

An Approach to Intrusion Detection by Means of Idiotypic Networks Paradigm

In this paper we present a novel intrusion detection architecture based on Idiotypic Network Theory (INIDS), that aims at dealing with large scale network attacks featuring variable properties, like Denial of Service (DoS). The proposed architecture performs dynamic and adaptive clustering of the network traffic for taking fast and effective countermeasures against such high-volume attacks. INIDS is evaluated on the MITpsila99 dataset and outperforms previous approaches for DoS detection applied to this set