Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Parallel genetic programming: a scalable implementation using the transputer network architecture
Advances in genetic programming
Fundamentals of fault-tolerant distributed computing in asynchronous environments
ACM Computing Surveys (CSUR)
A survey of rollback-recovery protocols in message-passing systems
ACM Computing Surveys (CSUR)
Some Considerations on the Reason for Bloat
Genetic Programming and Evolvable Machines
Managing Checkpoints for Parallel Programs
IPPS '96 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
CAGE: A Tool for Parallel Genetic Programming Applications
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
CALYPSO: a novel software system for fault-tolerant parallel processing on distributed platforms
HPDC '95 Proceedings of the 4th IEEE International Symposium on High Performance Distributed Computing
BOINC: A System for Public-Resource Computing and Storage
GRID '04 Proceedings of the 5th IEEE/ACM International Workshop on Grid Computing
Spatially Structured Evolutionary Algorithms: Artificial Evolution in Space and Time (Natural Computing Series)
A Performability-Oriented Software Rejuvenation Framework for Distributed Applications
DSN '05 Proceedings of the 2005 International Conference on Dependable Systems and Networks
A Fault Tolerant Optimization Algorithm based on Evolutionary Computation
DEPCOS-RELCOMEX '06 Proceedings of the International Conference on Dependability of Computer Systems
A large-scale study of failures in high-performance computing systems
DSN '06 Proceedings of the International Conference on Dependable Systems and Networks
A comparison of bloat control methods for genetic programming
Evolutionary Computation
Blocking vs. non-blocking coordinated checkpointing for large-scale fault tolerant MPI
Proceedings of the 2006 ACM/IEEE conference on Supercomputing
Characterizing resource availability in enterprise desktop grids
Future Generation Computer Systems
Is the island model fault tolerant?
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Automated design of image operators that detect interest points
Evolutionary Computation
Customizable execution environments with virtual desktop grid computing
PDCS '07 Proceedings of the 19th IASTED International Conference on Parallel and Distributed Computing and Systems
Characterizing fault tolerance in genetic programming
Future Generation Computer Systems
Characterizing fault-tolerance of genetic algorithms in desktop grid systems
EvoCOP'10 Proceedings of the 10th European conference on Evolutionary Computation in Combinatorial Optimization
Convergence analysis of evolutionary algorithms in the presence of crash-faults and cheaters
Computers & Mathematics with Applications
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Evolutionary Algorithms (EAs), and particularly Genetic Programming (GP), are techniques frequently employed to solve difficult real-life problems, which can require up to days or months of computation. One approach to reduce the time to solution is to use parallel computing on distributed platforms. Distributed platforms are prone to failures, and when these platforms are large and/or low-cost, failures are expected events rather than catastrophic exceptions. Therefore, fault tolerance and recovery techniques often become necessary. It turns out that Parallel GP (PGP) applications have an inherent ability to tolerate failures. This ability is quantified via simulation experiments performed using failure traces from real-world distributed platforms, namely, desktop grids (DGs), for two well-known GP problems. A simple technique is then proposed by which PGP applications can better tolerate the different, and often high, failures rates seen in different platforms.