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Asynchronous Evolutionary Multi-Objective Algorithms with heterogeneous evaluation costs

Mouadh Yagoubi 1, * Ludovic Thobois 2 Marc Schoenauer 1, 3
* Corresponding author
1 TAO - Machine Learning and Optimisation
CNRS - Centre National de la Recherche Scientifique : UMR8623, Inria Saclay - Ile de France, UP11 - Université Paris-Sud - Paris 11, LRI - Laboratoire de Recherche en Informatique
Abstract : Master-slave parallelization of Evolutionary Algorithms (EAs) is straightforward, by distributing all fitness computations to slaves. The benefits of asynchronous steady state approaches are well-known when facing a possible heterogeneity among the evaluation costs in term of runtime, be they due to heterogeneous hardware or non-linear numerical simulations. However, when this heterogeneity depends on some characteristics of the individuals being evaluated, the search might be biased, and some regions of the search space poorly explored. Motivated by a real-world case study of multi-objective optimization problem the optimization of the combustion in a Diesel Engine the consequences of different components of heterogeneity in the evaluation costs on the convergence of two Evolutionary Multi-objective Optimization Algorithms are investigated on artificially-heterogeneous benchmark problems. In some cases, better spread of the population on the Pareto front seem to result from the interplay between the heterogeneity at hand and the evolutionary search.
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Mouadh Yagoubi, Ludovic Thobois, Marc Schoenauer. Asynchronous Evolutionary Multi-Objective Algorithms with heterogeneous evaluation costs. IEEE Congress on Evolutionary Computation, CEC 2011, New Orleans, LA, USA, 5-8 June, 2011, Jun 2011, New Orleans, LA, United States. pp.21-28. ⟨hal-00625318⟩

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