JADE, an adaptive differential evolution algorithm, benchmarked on the BBOB noiseless testbed

Pošík, Petr; Klemš, Václav

doi:10.1145/2330784.2330814

Cited by 19 publications

(16 citation statements)

References 7 publications

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“…For algorithms which are invariant per rotation (not DE, not PSO) this does not make any difference. (c) Adaptive methods for choosing parameters might be tested for PSO or DE [42,28,31,7] as they could maybe handle better the extreme size of our problems. (d) We tested the addition of completely useless variables.…”

Section: Resultsmentioning

confidence: 99%

On the Codimension of the Set of Optima: Large Scale Optimisation with Few Relevant Variables

Berthier

Teytaud

2016

Lecture Notes in Computer Science

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Abstract. The complexity of continuous optimisation by comparisonbased algorithms has been developed in several recent papers.Roughly speaking, these papers conclude that a precision can be reached with cost Θ(n log(1/ )) in dimension n within polylogarithmic factors for the sphere function. Compared to other (non comparison-based) algorithms, this rate is not excellent; on the other hand, it is classically considered that comparison-based algorithms have some robustness advantages, as well as scalability on parallel machines and simplicity. In the present paper we show another advantage, namely resilience to useless variables, thanks to a complexity bound Θ(m log(1/ )) where m is the codimension of the set of optima, possibly m << n. In addition, experiments show that some evolutionary algorithms have a negligible computational complexity even in high dimension, making them practical for huge problems with many useless variables.

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Section: Resultsmentioning

confidence: 99%

On the Codimension of the Set of Optima: Large Scale Optimisation with Few Relevant Variables

Berthier

Teytaud

2016

Lecture Notes in Computer Science

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“…Their performance is compared to the new DE/rand/rand/1/acm strategy ('acm') and a variant of JADE algorithm without archive ('JADE') [12] with asynchronous DE/rand/rand/1/bin strategy. The latter algorithm uses an adaptive scheme for crossover rate and was shown to have better performance compared to other adaptive DE algorithms [12,5]. For all four strategies Fmin = 0.1, Fmax = 1.0, cF = 0.01, σF = 0.1, initial µF = 0.5 and N min p = 10 are used.…”

Section: Numerical Tests and Resultsmentioning

confidence: 99%

Asynchronous differential evolution with adaptive correlation matrix

Zhabitsky

Zhabitskaya

2013

Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation

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Differential evolution (DE) is an efficient algorithm to solve global optimization problems. It has a simple internal structure and uses a few control parameters. In this paper we incorporate crossover based on adaptive correlation matrix into Asynchronous differential evolution (ADE). Thanks to the proposed crossover the novel algorithm automatically adapts to the landscape of the optimized objective function. Combined with an adaptive scheme for the mutation scale factor and an automatic inflation of the population size this results in quasi parameter-free algorithm from the user's point of view. The performance of the Asynchronous differential evolution with adaptive correlation matrix is reported on the set of BBOB-2012 benchmark functions.

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“…Similar population size was also applied in [10]. The crossover strategy is the exponential one and the mutation operator combines the best member with other two randomly chosen individuals.…”

Section: Methodsmentioning

confidence: 99%

Benchmarking a hybrid multi level single linkagealgorithm on the bbob noiseless testbed

Pál

2013

Proceedings of the 15th Annual Conference Companion on Genetic and Evolutionary Computation

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Multi Level Single Linkage (MLSL) is a well known stochastic global optimization method. In this paper, a new hybrid variant (HMLSL) of the MLSL algorithm is presented. The most important improvements are related to the sampling phase: the sample is generated from a Sobol quasi-random sequence and a few percent of the population is further improved by using crossover and mutation operators like in a traditional differential evolution (DE) method.The aim of this study is to evaluate the performance of the new HMLSL algorithm on the testbed of 24 noiseless functions. The new algorithm is also compared against a simple MLSL and a traditional DE in order to identify the benefits of the applied improvements.The results confirm that the HMLSL outperforms the MLSL and DE methods. The new method has a larger probability of success and usually is faster especially in the final stage of the optimization than the other two algorithms.

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JADE, an adaptive differential evolution algorithm, benchmarked on the BBOB noiseless testbed

Cited by 19 publications

References 7 publications

On the Codimension of the Set of Optima: Large Scale Optimisation with Few Relevant Variables

On the Codimension of the Set of Optima: Large Scale Optimisation with Few Relevant Variables

Asynchronous differential evolution with adaptive correlation matrix

Benchmarking a hybrid multi level single linkagealgorithm on the bbob noiseless testbed

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