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Evolutionary Computation
Journal Prestige (SJR): 0.792

Citation Impact (citeScore): 3
Number of Followers: 12

Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1063-6560 - ISSN (Online) 1530-9304
Published by MIT Press

[39 journals]

Stagnation Detection with Randomized Local Search *
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  Pages: 1 - 29
  Abstract: AbstractRecently a mechanism called stagnation detection was proposed that automatically adjusts the mutation rate of evolutionary algorithms when they encounter local optima. The so-called SD-(1+1) EA introduced by Rajabi and Witt (2022) adds stagnation detection to the classical (1+1) EA with standard bit mutation. This algorithm flips each bit independently with some mutation rate, and stagnation detection raises the rate when the algorithm is likely to have encountered a local optimum. In this article, we investigate stagnation detection in the context of the k-bit flip operator of randomized local search that flips k bits chosen uniformly at random and let stagnation detection adjust the parameter k. We obtain improved runtime results compared with the SD-(1+1) EA amounting to a speedup of at least (1-o(1))2πm, where m is the so-called gap size, that is, the distance to the next improvement. Moreover, we propose additional schemes that prevent infinite optimization times even if the algorithm misses a working choice of k due to unlucky events. Finally, we present an example where standard bit mutation still outperforms the k-bit flip operator with stagnation detection.
  PubDate: Wed, 01 Mar 2023 00:00:00 GMT
  DOI: 10.1162/evco_a_00313
  Issue No: Vol. 31, No. 1 (2023)
Hybridization of Evolutionary Operators with Elitist Iterated Racing for
the Simulation Optimization of Traffic Lights Programs
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  Pages: 31 - 51
  Abstract: AbstractIn the traffic light scheduling problem, the evaluation of candidate solutions requires the simulation of a process under various (traffic) scenarios. Thus, good solutions should not only achieve good objective function values, but they must be robust (low variance) across all different scenarios. Previous work has shown that combining IRACE with evolutionary operators is effective for this task due to the power of evolutionary operators in numerical optimization. In this article, we further explore the hybridization of evolutionary operators and the elitist iterated racing of IRACE for the simulation–optimization of traffic light programs. We review previous works from the literature to find the evolutionary operators performing the best when facing this problem to propose new hybrid algorithms. We evaluate our approach over a realistic case study derived from the traffic network of Málaga (Spain) with 275 traffic lights that should be scheduled optimally. The experimental analysis reveals that the hybrid algorithm comprising IRACE plus differential evolution offers statistically better results than the other algorithms when the budget of simulations is low. In contrast, IRACE performs better than the hybrids for a high simulations budget, although the optimization time is much longer.
  PubDate: Wed, 01 Mar 2023 00:00:00 GMT
  DOI: 10.1162/evco_a_00314
  Issue No: Vol. 31, No. 1 (2023)
An Uncertainty Measure for Prediction of Non-Gaussian Process Surrogates
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  Pages: 53 - 71
  Abstract: AbstractModel management is an essential component in data-driven surrogate-assisted evolutionary optimization. In model management, the solutions with a large degree of uncertainty in approximation play an important role. They can strengthen the exploration ability of algorithms and improve the accuracy of surrogates. However, there is no theoretical method to measure the uncertainty of prediction of Non-Gaussian process surrogates. To address this issue, this article proposes a method to measure the uncertainty. In this method, a stationary random field with a known zero mean is used to measure the uncertainty of prediction of Non-Gaussian process surrogates. Based on experimental analyses, this method is able to measure the uncertainty of prediction of Non-Gaussian process surrogates. The method's effectiveness is demonstrated on a set of benchmark problems in single surrogate and ensemble surrogates cases.
  PubDate: Wed, 01 Mar 2023 00:00:00 GMT
  DOI: 10.1162/evco_a_00316
  Issue No: Vol. 31, No. 1 (2023)

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