Understanding Simple Asynchronous Evolutionary Algorithms

In many applications of evolutionary algorithms, the time required to evaluate the fitness of individuals is long and variable. When the variance in individual evaluation times is non-negligible, traditional, synchronous master-slave EAs incur idle time in CPU resources. An asynchronous approach to parallelization of EAs promises to eliminate idle time and thereby to reduce the amount of wall-clock time it takes to solve a problem. However, the behavior of asynchronous evolutionary algorithms is not well understood. In particular, it is not clear exactly how much faster the asynchronous algorithm will tend to run, or whether its evolutionary trajectory may follow a sub-optimal search path that cancels out the promised benefits. This paper presents a preliminary analysis of simple asynchronous EA performance in terms of speed and problem-solving ability.

[1]  Juan Julián Merelo Guervós,et al.  Designing and testing a pool-based evolutionary algorithm , 2012, Natural Computing.

[2]  Brian D. Davison,et al.  Effect of global parallelism on the behavior of a steady state genetic algorithm for design optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[3]  Enrique Alba,et al.  Parallelism and evolutionary algorithms , 2002, IEEE Trans. Evol. Comput..

[4]  Jinwoo Kim,et al.  Hierarchical asynchronous genetic algorithms for parallel/distributed simulation-based optimization , 1995 .

[5]  Bernard P. Zeigler,et al.  Asynchronous Genetic Algorithms on Parallel Computers , 1993, ICGA.

[6]  Edwin Lughofer,et al.  On the Performance of Master-Slave Parallelization Methods for Multi-Objective Evolutionary Algorithms , 2013, ICAISC.

[7]  Marc Schoenauer,et al.  Asynchronous Evolutionary Multi-Objective Algorithms with heterogeneous evaluation costs , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[8]  Cho-Li Wang,et al.  A New Asynchronous Parallel Evolutionary Algorithm for Function Optimization , 2002, PPSN.

[9]  Gary B. Parker,et al.  Using a Queue Genetic Algorithm to Evolve Xpilot Control Strategies on a Distributed System , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[10]  Thomas Stützle,et al.  Parallelization Strategies for Ant Colony Optimization , 1998, PPSN.

[11]  Andreas Zell,et al.  Median-Selection for Parallel Steady-State Evolution Strategies , 2000, PPSN.

[12]  Trevor N. Mudge,et al.  A Parallel Genetic Algorithm for Multiobjective Microprocessor Design , 1995, ICGA.

[13]  Byung-Il Koh,et al.  Parallel asynchronous particle swarm optimization , 2006, International journal for numerical methods in engineering.

[14]  Kenneth de Jong,et al.  Generation gap methods , 2018, Evolutionary Computation 1.

[15]  El-Ghazali Talbi,et al.  Hierarchical parallel approach for GSM mobile network design , 2006, J. Parallel Distributed Comput..

[16]  Matjaz Depolli,et al.  Asynchronous Master-Slave Parallelization of Differential Evolution for Multi-Objective Optimization , 2013, Evolutionary Computation.

[17]  Stefano Cagnoni,et al.  GPU-based asynchronous particle swarm optimization , 2011, GECCO '11.

[18]  Kenneth A. De Jong,et al.  Generation Gaps Revisited , 1992, FOGA.

[19]  Jaroslaw Sobieszczanski-Sobieski,et al.  A Parallel Particle Swarm Optimization Algorithm Accelerated by Asynchronous Evaluations , 2005 .

[20]  Erick Cantú-Paz,et al.  A Survey of Parallel Genetic Algorithms , 2000 .

[21]  Andrew Philippides,et al.  Tool sequence optimization using synchronous and asynchronous parallel multi-objective evolutionary algorithms with heterogeneous evaluations , 2013, 2013 IEEE Congress on Evolutionary Computation.

[22]  Marc Schoenauer,et al.  An asynchronous steady-state NSGA-II algorithm for multi-objective optimization of Diesel combustion , 2010 .

[23]  Enrique Alba,et al.  Analyzing synchronous and asynchronous parallel distributed genetic algorithms , 2001, Future Gener. Comput. Syst..

[24]  Riccardo Poli,et al.  Parallel genetic algorithm taxonomy , 1999, 1999 Third International Conference on Knowledge-Based Intelligent Information Engineering Systems. Proceedings (Cat. No.99TH8410).

[25]  Thomas Sttitzle.  Parallelization Strategies for Ant Colony Optimization , 2006 .

[26]  Thomas Bartz-Beielstein,et al.  Experimental Research in Evolutionary Computation - The New Experimentalism , 2010, Natural Computing Series.

[27]  Colin R. Reeves,et al.  Evolutionary computation: a unified approach , 2007, Genetic Programming and Evolvable Machines.

[28]  Enrique Alba,et al.  Selection Intensity in Asynchronous Cellular Evolutionary Algorithms , 2003, GECCO.

[29]  Enrique Alba,et al.  A study of master-slave approaches to parallelize NSGA-II , 2008, 2008 IEEE International Symposium on Parallel and Distributed Processing.

[30]  Thomas Philip Runarsson,et al.  An Asynchronous Parallel Evolution Strategy , 2003, Int. J. Comput. Intell. Appl..

[31]  Janez Puhan,et al.  A new asynchronous parallel global optimization method based on simulated annealing and differential evolution , 2011, Appl. Soft Comput..

[32]  Erick Cantú-Paz,et al.  Efficient and Accurate Parallel Genetic Algorithms , 2000, Genetic Algorithms and Evolutionary Computation.