Parallel recombinative simulated annealing: a genetic algorithm
Parallel Computing
Analyzing synchronous and asynchronous parallel distributed genetic algorithms
Future Generation Computer Systems - Special issue on bio-impaired solutions to parallel processing problems
Computers and Intractability; A Guide to the Theory of NP-Completeness
Computers and Intractability; A Guide to the Theory of NP-Completeness
A Taxonomy of Hybrid Metaheuristics
Journal of Heuristics
Heterogeneous computing and parallel genetic algorithms
Journal of Parallel and Distributed Computing - Problems in parallel and distributed computing: Solutions based on evolutionary paradigms
Modelling and Simulation of Distributed Evolutionary Search Processes for Function Optimization
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Parallel Simulated Annealing and Genetic Algorithms: a Space of Hybrid Methods
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Parallel heterogeneous genetic algorithms for continuous optimization
Parallel Computing - Special issue: Parallel and nature-inspired computational paradigms and applications
Real-coded memetic algorithms with crossover hill-climbing
Evolutionary Computation - Special issue on magnetic algorithms
Where the really hard problems are
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
Using landscape measures for the online tuning of heterogeneous distributed gas
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Large-step markov chains for the TSP incorporating local search heuristics
Operations Research Letters
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In this article we present Ethane, a parallel heterogeneous metaheuristic model specifically designed for its execution on heterogeneous hardware environments. With Ethane we propose a hybrid parallel search algorithm inspired in the structure of the chemical compound of the same name, implementing a heterogeneous island model based in the structure of the chemical bonds of the ethane compound. Here we also shape a schema for describing a complete family of parallel heterogeneous metaheuristics inspired by the structure of hydrocarbons in nature, HydroCM (HydroCarbon inspired Metaheuristics), establishing a resemblance between atoms and computers, and between chemical bonds and communication links. Our goal is to gracefully match computers of different computing power to algorithms of different behavior (genetic algorithm and simulated annealing in this study), all them collaborating to solve the same problem. In addition to the nice natural metaphor we will show that Ethane, though simple, can solve search problems in a faster and more robust way than well-known panmictic and distributed algorithms very popular in the literature, as well as can achieve a better exploration/exploitation balance during the search process.